Task: Finding similar computer science papers to read based on the similarity between titles using TF-IDF
What is TF-IDF (Term Frequency - Inverse Document Frequency)
We’ve got one file per paper which contains the title of the paper. We first need to iterate through that directory and build an array containing the papers:
In [91]:
import glob
corpus = []
for file in glob.glob("data/papers/*.txt"):
with open(file, "r") as paper:
corpus.append((file, paper.read()))
Check the titles of the first 10 papers.
In [92]:
corpus[0:10]
Out[92]:
[('data/papers/100000.txt', 'Inside risks: the clock grows at midnight'),
('data/papers/100226.txt', 'Coherent functions and program checkers'),
('data/papers/100228.txt', 'The wakeup problem'),
('data/papers/100231.txt', 'Efficient robust parallel computations'),
('data/papers/100262.txt',
'An optimal algorithm for on-line bipartite matching'),
('data/papers/100269.txt',
'One-way functions are necessary and sufficient for secure signatures'),
('data/papers/100270.txt',
'Pseudo-random generators under uniform assumptions'),
('data/papers/100272.txt',
'Witness indistinguishable and witness hiding protocols'),
('data/papers/100273.txt',
'Public-key cryptosystems provably secure against chosen ciphertext attacks'),
('data/papers/100287.txt', 'The round complexity of secure protocols')]
Next we’ll build a TF/IDF matrix for each paper:
In [93]:
from sklearn.feature_extraction.text import TfidfVectorizer
tf = TfidfVectorizer(analyzer='word', ngram_range=(1,3), min_df = 0, stop_words = 'english')
tfidf_matrix = tf.fit_transform([content for file, content in corpus[:]])
Next we’ll write a function that will find us the top n similar papers based on cosine similarity:
In [94]:
from sklearn.metrics.pairwise import linear_kernel
def find_similar(tfidf_matrix, index, top_n = 5):
cosine_similarities = linear_kernel(tfidf_matrix[index:index+1], tfidf_matrix).flatten()
related_docs_indices = [i for i in cosine_similarities.argsort()[::-1] if i != index]
return [(index, cosine_similarities[index]) for index in related_docs_indices][0:top_n]
Let’s try it out:
In [95]:
corpus[1619]
Out[95]:
('data/papers/221215.txt',
'TOTEM: a reliable ordered delivery protocol for interconnected local-area networks')
In [96]:
for index, score in find_similar(tfidf_matrix, 1619):
print(score, corpus[index])
0.917540397202 ('data/papers/852338.txt', 'A reliable ordered delivery protocol for interconnected local area networks')
0.248736845733 ('data/papers/800897.txt', 'Interconnection of broadband local area networks')
0.207309089025 ('data/papers/103726.txt', 'High-speed local area networks and their performance: a survey')
0.204166719869 ('data/papers/161736.txt', 'High-speed switch scheduling for local-area networks')
0.198514433132 ('data/papers/627363.txt', 'Algorithms for Distributed Query Processing in Broadcast Local Area Networks')
It’s pretty good for finding duplicate papers!
In [97]:
corpus[1599]
Out[97]:
('data/papers/217470.txt',
'A reliable multicast framework for light-weight sessions and application level framing')
In [98]:
for index, score in find_similar(tfidf_matrix, 1599):
print (score, corpus[index])
1.0 ('data/papers/270863.txt', 'A reliable multicast framework for light-weight sessions and application level framing')
0.139643354066 ('data/papers/218325.txt', 'The KryptoKnight family of light-weight protocols for authentication and key distribution')
0.134763799612 ('data/papers/1251445.txt', 'ALMI: an application level multicast infrastructure')
0.117630311817 ('data/papers/125160.txt', 'Ordered and reliable multicast communication')
0.117630311817 ('data/papers/128741.txt', 'Ordered and reliable multicast communication')
But sometimes it identifies duplicates that aren’t identical:
In [99]:
corpus[5784]
Out[99]:
('data/papers/RFC2616.txt', 'Hypertext Transfer Protocol -- HTTP/1.1')
In [100]:
for index, score in find_similar(tfidf_matrix, 5784):
print (score, corpus[index])
1.0 ('data/papers/RFC1945.txt', 'Hypertext Transfer Protocol -- HTTP/1.0')
1.0 ('data/papers/RFC2068.txt', 'Hypertext Transfer Protocol -- HTTP/1.1')
0.232865694216 ('data/papers/131844.txt', 'XTP: the Xpress Transfer Protocol')
0.138876842331 ('data/papers/RFC1866.txt', 'Hypertext Markup Language - 2.0')
0.104775586915 ('data/papers/760249.txt', 'On the transfer of control between contexts')
Finally, let us creat a CSV file containing to 5 similar papers for each paper.
In [108]:
import csv
with open("./output/similarities.csv", "w") as similarities_file:
writer = csv.writer(similarities_file, delimiter = ",")
for me_index, item in enumerate(corpus):
similar_documents = [(corpus[index], score) for index, score in find_similar(tfidf_matrix, me_index)]
me = corpus[me_index]
document_id = me[0].split("/")[2].split(".")[0]
for ((raw_similar_document_id, title), score) in similar_documents:
similar_document_id = raw_similar_document_id.split("/")[2].split(".")[0]
writer.writerow([document_id, me[1], similar_document_id, title, score])
In [ ]:
Print 5 top similar papers for each paper as long as the similarity is greater than 0.5.
In [111]:
items = []
with open("./output/similarities.csv", "r") as similarities_file:
reader = csv.reader(similarities_file, delimiter = ",")
for row in reader:
lst = list(row)
lst[4] = float(lst[4])
items.append(tuple(lst))
by_similarity = sorted(items, key = lambda x: x[4], reverse = True)
for similar_item in [item for item in by_similarity if 0.5 < item[4] < 0.99]:
print (similar_item)
print ('\n')
('221215', 'TOTEM: a reliable ordered delivery protocol for interconnected local-area networks', '852338', 'A reliable ordered delivery protocol for interconnected local area networks', 0.917540397202)
('852338', 'A reliable ordered delivery protocol for interconnected local area networks', '221215', 'TOTEM: a reliable ordered delivery protocol for interconnected local-area networks', 0.917540397202)
('146935', 'The generalized tree quorum protocol: an efficient approach for managing replicated data', '671977', 'The Tree Quorum Protocol: An Efficient Approach for Managing Replicated Data', 0.914810942048)
('146935', 'The generalized tree quorum protocol: an efficient approach for managing replicated data', '94419', 'The tree quorum protocol: an efficient approach for managing replicated data', 0.914810942048)
('671977', 'The Tree Quorum Protocol: An Efficient Approach for Managing Replicated Data', '146935', 'The generalized tree quorum protocol: an efficient approach for managing replicated data', 0.914810942048)
('94419', 'The tree quorum protocol: an efficient approach for managing replicated data', '146935', 'The generalized tree quorum protocol: an efficient approach for managing replicated data', 0.914810942048)
('2210', 'Security for computer networks: an introduction to data security in teleprocessing and electronic funds transfer', '74790', 'Security for computer networks: and introduction to data security in teleprocessing and electronic funds transfer (2nd ed.)', 0.906760756541)
('74790', 'Security for computer networks: and introduction to data security in teleprocessing and electronic funds transfer (2nd ed.)', '2210', 'Security for computer networks: an introduction to data security in teleprocessing and electronic funds transfer', 0.906760756541)
('274443', 'Concurrency control: methods, performance, and analysis', '525573', 'Database Concurrency Control: Methods, Performance, and Analysis', 0.901000522385)
('525573', 'Database Concurrency Control: Methods, Performance, and Analysis', '274443', 'Concurrency control: methods, performance, and analysis', 0.901000522385)
('206343', 'A Total Ordering Multicast Protocol Using Propagation Trees', '270952', 'A Comment on "A Total Ordering Multicast Protocol Using Propagation Trees"', 0.893897080002)
('270952', 'A Comment on "A Total Ordering Multicast Protocol Using Propagation Trees"', '206343', 'A Total Ordering Multicast Protocol Using Propagation Trees', 0.893897080002)
('RFC1057', 'RPC: Remote Procedure Call Protocol specification', 'RFC1831', 'RPC: Remote Procedure Call Protocol Specification Version 2', 0.892676776768)
('RFC1831', 'RPC: Remote Procedure Call Protocol Specification Version 2', 'RFC1057', 'RPC: Remote Procedure Call Protocol specification', 0.892676776768)
('593438', 'Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals', '655593', 'Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total', 0.889932379826)
('655593', 'Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total', '593438', 'Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals', 0.889932379826)
('323610', 'Simple constant-time consensus protocols in realistic failure models (extended abstract)', '65956', 'Simple constant-time consensus protocols in realistic failure models', 0.888031855671)
('65956', 'Simple constant-time consensus protocols in realistic failure models', '323610', 'Simple constant-time consensus protocols in realistic failure models (extended abstract)', 0.888031855671)
('3319', 'Implementation of resilient, atomic data types', '806851', 'Specification and implementation of resilient, atomic data types', 0.87758184826)
('806851', 'Specification and implementation of resilient, atomic data types', '3319', 'Implementation of resilient, atomic data types', 0.87758184826)
('75583', 'Dynamic file migration in distributed computer systems', '910946', 'File migration in distributed computer systems', 0.870608411265)
('910946', 'File migration in distributed computer systems', '75583', 'Dynamic file migration in distributed computer systems', 0.870608411265)
('347560', 'Practical network support for IP traceback', '383578', 'Network support for IP traceback', 0.853839405191)
('383578', 'Network support for IP traceback', '347560', 'Practical network support for IP traceback', 0.853839405191)
('384222', 'Using Cohort Scheduling to Enhance Server Performance (Extended Abstract)', '713864', 'Using Cohort-Scheduling to Enhance Server Performance', 0.85333188992)
('713864', 'Using Cohort-Scheduling to Enhance Server Performance', '384222', 'Using Cohort Scheduling to Enhance Server Performance (Extended Abstract)', 0.85333188992)
('55604', 'The limited performance benefits of migrating active processes for load sharing', '893796', 'A note on "The limited performance benefits of migrating active processes for load', 0.852582147746)
('893796', 'A note on "The limited performance benefits of migrating active processes for load', '55604', 'The limited performance benefits of migrating active processes for load sharing', 0.852582147746)
('358824', 'Experience with processes and monitors in Mesa', '806568', 'Experience with processes and monitors in Mesa (Summary)', 0.84800689577)
('806568', 'Experience with processes and monitors in Mesa (Summary)', '358824', 'Experience with processes and monitors in Mesa', 0.84800689577)
('823227', 'WebOS: Operating System Services for Wide Area Applications', '928428', 'Operating system services for wide-area applications', 0.84675559185)
('928428', 'Operating system services for wide-area applications', '823227', 'WebOS: Operating System Services for Wide Area Applications', 0.84675559185)
('191872', 'Optimization of dynamic query evaluation plans', '66960', 'Dynamic query evaluation plans', 0.846187354814)
('66960', 'Dynamic query evaluation plans', '191872', 'Optimization of dynamic query evaluation plans', 0.846187354814)
('190326', 'An architecture for wide-area multicast routing', '225995', 'The PIM architecture for wide-area multicast routing', 0.843706579771)
('225995', 'The PIM architecture for wide-area multicast routing', '190326', 'An architecture for wide-area multicast routing', 0.843706579771)
('823140', 'Packing Messages as a Tool for Boosting the Performance of Total Ordering Protocls', '866791', 'Packing Messages as a Tool for Boosting the Performance of Total Ordering Protocols', 0.841506946386)
('866791', 'Packing Messages as a Tool for Boosting the Performance of Total Ordering Protocols', '823140', 'Packing Messages as a Tool for Boosting the Performance of Total Ordering Protocls', 0.841506946386)
('502053', 'Storage management and caching in PAST, a large-scale, persistent peer-to-peer storage utility', '876400', 'PAST: A Large-Scale, Persistent Peer-to-Peer Storage Utility', 0.837891224593)
('876400', 'PAST: A Large-Scale, Persistent Peer-to-Peer Storage Utility', '502053', 'Storage management and caching in PAST, a large-scale, persistent peer-to-peer storage utility', 0.837891224593)
('341817', 'DCAS-based concurrent deques', '758425', 'Even Better DCAS-Based Concurrent Deques', 0.837019769295)
('758425', 'Even Better DCAS-Based Concurrent Deques', '341817', 'DCAS-based concurrent deques', 0.837019769295)
('15418', 'Availability in partitioned replicated databases', '866260', 'Maintaining Availability in Partitioned Replicated Databases', 0.828284580359)
('15418', 'Availability in partitioned replicated databases', '63501', 'Maintaining availability in partitioned replicated databases', 0.828284580359)
('63501', 'Maintaining availability in partitioned replicated databases', '866116', 'Availability in partitioned replicated databases', 0.828284580359)
('63501', 'Maintaining availability in partitioned replicated databases', '15418', 'Availability in partitioned replicated databases', 0.828284580359)
('866116', 'Availability in partitioned replicated databases', '866260', 'Maintaining Availability in Partitioned Replicated Databases', 0.828284580359)
('866116', 'Availability in partitioned replicated databases', '63501', 'Maintaining availability in partitioned replicated databases', 0.828284580359)
('866260', 'Maintaining Availability in Partitioned Replicated Databases', '866116', 'Availability in partitioned replicated databases', 0.828284580359)
('866260', 'Maintaining Availability in Partitioned Replicated Databases', '15418', 'Availability in partitioned replicated databases', 0.828284580359)
('62588', 'Automatically increasing the fault-tolerance of distributed systems', '83337', 'Automatically increasing the fault-tolerance of distributed algorithms', 0.827657268472)
('83337', 'Automatically increasing the fault-tolerance of distributed algorithms', '62588', 'Automatically increasing the fault-tolerance of distributed systems', 0.827657268472)
('320260', 'System level concurrency control for distributed database systems', '61076', 'Concurrency control in distributed database systems', 0.82608883796)
('320260', 'System level concurrency control for distributed database systems', '356846', 'Concurrency Control in Distributed Database Systems', 0.82608883796)
('356846', 'Concurrency Control in Distributed Database Systems', '320260', 'System level concurrency control for distributed database systems', 0.82608883796)
('61076', 'Concurrency control in distributed database systems', '320260', 'System level concurrency control for distributed database systems', 0.82608883796)
('357355', 'Implementing atomic actions on decentralized data', '806584', 'Implementing atomic actions on decentralized data (Extended Abstract)', 0.819970951765)
('806584', 'Implementing atomic actions on decentralized data (Extended Abstract)', '357355', 'Implementing atomic actions on decentralized data', 0.819970951765)
('3831', 'A mean value performance model for locking in databases: the no-waiting case', '911561', 'A mean value performance model for locking in databases', 0.817072143979)
('911561', 'A mean value performance model for locking in databases', '3831', 'A mean value performance model for locking in databases: the no-waiting case', 0.817072143979)
('699395', 'Exploiting Atomic Broadcast in Replicated Databases (Extended Abstract)', '699991', 'Exploiting Atomic Broadcast in Replicated Databases', 0.816153445451)
('699991', 'Exploiting Atomic Broadcast in Replicated Databases', '699395', 'Exploiting Atomic Broadcast in Replicated Databases (Extended Abstract)', 0.816153445451)
('214406', 'A distributed mutual exclusion algorithm', '675172', 'A Robust Distributed Mutual Exclusion Algorithm', 0.813768439168)
('675172', 'A Robust Distributed Mutual Exclusion Algorithm', '214406', 'A distributed mutual exclusion algorithm', 0.813768439168)
('166255', 'On the self-similar nature of Ethernet traffic', '178222', 'On the self-similar nature of Ethernet traffic (extended version)', 0.808891933127)
('178222', 'On the self-similar nature of Ethernet traffic (extended version)', '166255', 'On the self-similar nature of Ethernet traffic', 0.808891933127)
('135451', 'The weakest failure detector for solving consensus', '831089', 'Optimal Implementation of the Weakest Failure Detector for Solving Consensus', 0.80695835826)
('234549', 'The weakest failure detector for solving consensus', '831089', 'Optimal Implementation of the Weakest Failure Detector for Solving Consensus', 0.80695835826)
('831089', 'Optimal Implementation of the Weakest Failure Detector for Solving Consensus', '135451', 'The weakest failure detector for solving consensus', 0.80695835826)
('831089', 'Optimal Implementation of the Weakest Failure Detector for Solving Consensus', '234549', 'The weakest failure detector for solving consensus', 0.80695835826)
('22180', 'An O(lg n) expected rounds randomized Byzantine generals protocol', '42229', 'An O(log n) expected rounds randomized byzantine generals protocol', 0.803017670827)
('42229', 'An O(log n) expected rounds randomized byzantine generals protocol', '22180', 'An O(lg n) expected rounds randomized Byzantine generals protocol', 0.803017670827)
('209169', 'DB2 parallel edition', '223876', 'An overview of DB2 parallel edition', 0.802511440926)
('223876', 'An overview of DB2 parallel edition', '209169', 'DB2 parallel edition', 0.802511440926)
('RFC1813', 'NFS Version 3 Protocol Specification', 'RFC3010', 'NFS version 4 Protocol', 0.801686837764)
('RFC3010', 'NFS version 4 Protocol', 'RFC1813', 'NFS Version 3 Protocol Specification', 0.801686837764)
('319717', 'Distributed deadlock detection algorithm', '357365', 'Distributed deadlock detection', 0.798073875004)
('357365', 'Distributed deadlock detection', '319717', 'Distributed deadlock detection algorithm', 0.798073875004)
('237140', 'The case for a single-chip multiprocessor', '620806', 'A Single-Chip Multiprocessor', 0.796091549448)
('620806', 'A Single-Chip Multiprocessor', '237140', 'The case for a single-chip multiprocessor', 0.796091549448)
('52355', 'A binary feedback scheme for congestion avoidance in computer networks with a connectionless network layer', '59321', 'Congestion avoidance in computer networks with a connectionless network layer', 0.795879407815)
('59321', 'Congestion avoidance in computer networks with a connectionless network layer', '52355', 'A binary feedback scheme for congestion avoidance in computer networks with a connectionless network layer', 0.795879407815)
('52355', 'A binary feedback scheme for congestion avoidance in computer networks with a connectionless network layer', '78955', 'A binary feedback scheme for congestion avoidance in computer networks', 0.794375851701)
('78955', 'A binary feedback scheme for congestion avoidance in computer networks', '52355', 'A binary feedback scheme for congestion avoidance in computer networks with a connectionless network layer', 0.794375851701)
('302435', 'Fault-tolerant broadcasts and related problems', '866693', 'A Modular Approach to Fault-Tolerant Broadcasts and Related Problems', 0.794260035015)
('866693', 'A Modular Approach to Fault-Tolerant Broadcasts and Related Problems', '302435', 'Fault-tolerant broadcasts and related problems', 0.794260035015)
('113407', 'Sequential consistency versus linearizability (extended abstract)', '176576', 'Sequential consistency versus linearizability', 0.793864678322)
('176576', 'Sequential consistency versus linearizability', '113407', 'Sequential consistency versus linearizability (extended abstract)', 0.793864678322)
('542899', 'Introduction to Mathematical Theory of Computation', '940279', 'Mathematical Theory of Computation', 0.793269349424)
('940279', 'Mathematical Theory of Computation', '542899', 'Introduction to Mathematical Theory of Computation', 0.793269349424)
('1313753', 'Detection of Mutual Inconsistency in Distributed Systems', '653383', 'Detection of Mutual Inconsistency in Distributed Databases', 0.792468133376)
('653383', 'Detection of Mutual Inconsistency in Distributed Databases', '1313753', 'Detection of Mutual Inconsistency in Distributed Systems', 0.792468133376)
('161469', 'A methodology for implementing highly concurrent data objects', '99185', 'A methodology for implementing highly concurrent data structures', 0.792186829402)
('99185', 'A methodology for implementing highly concurrent data structures', '161469', 'A methodology for implementing highly concurrent data objects', 0.792186829402)
('324128', 'Building a high-performance, programmable secure coprocessor', '728325', 'Using a High-Performance, Programmable Secure Coprocessor', 0.790696788918)
('728325', 'Using a High-Performance, Programmable Secure Coprocessor', '324128', 'Building a high-performance, programmable secure coprocessor', 0.790696788918)
('55516', 'Distributed shared memory in a loosely coupled distributed system', '916634', 'Distributed shared memory in a loosely-coupled environment', 0.787504924715)
('916634', 'Distributed shared memory in a loosely-coupled environment', '55516', 'Distributed shared memory in a loosely coupled distributed system', 0.787504924715)
('1313439', 'Proving the Correctness of Multiprocess Programs', '357068', 'A New Approach to Proving the Correctness of Multiprocess Programs', 0.783336181585)
('357068', 'A New Approach to Proving the Correctness of Multiprocess Programs', '1313439', 'Proving the Correctness of Multiprocess Programs', 0.783336181585)
('165662', 'Overview of the Vesta parallel file system', '233558', 'The Vesta parallel file system', 0.782857386059)
('233558', 'The Vesta parallel file system', '165662', 'Overview of the Vesta parallel file system', 0.782857386059)
('359585', 'Communicating sequential processes', '833', 'A Theory of Communicating Sequential Processes', 0.782174842929)
('3921', 'Communicating sequential processes', '833', 'A Theory of Communicating Sequential Processes', 0.782174842929)
('833', 'A Theory of Communicating Sequential Processes', '3921', 'Communicating sequential processes', 0.782174842929)
('833', 'A Theory of Communicating Sequential Processes', '359585', 'Communicating sequential processes', 0.782174842929)
('174657', 'The elusive atomic register', '41858', 'The elusive atomic register revisited', 0.780276585396)
('41858', 'The elusive atomic register revisited', '900438', 'The Elusive Atomic Register', 0.780276585396)
('41858', 'The elusive atomic register revisited', '174657', 'The elusive atomic register', 0.780276585396)
('900438', 'The Elusive Atomic Register', '41858', 'The elusive atomic register revisited', 0.780276585396)
('357110', 'A Proof System for Communicating Sequential Processes', '3921', 'Communicating sequential processes', 0.771859405517)
('357110', 'A Proof System for Communicating Sequential Processes', '359585', 'Communicating sequential processes', 0.771859405517)
('359585', 'Communicating sequential processes', '357110', 'A Proof System for Communicating Sequential Processes', 0.771859405517)
('3921', 'Communicating sequential processes', '357110', 'A Proof System for Communicating Sequential Processes', 0.771859405517)
('192033', 'EVENODD: an optimal scheme for tolerating double disk failures in RAID architectures', '203282', 'EVENODD: An Efficient Scheme for Tolerating Double Disk Failures in RAID Architectures', 0.769940351821)
('203282', 'EVENODD: An Efficient Scheme for Tolerating Double Disk Failures in RAID Architectures', '192033', 'EVENODD: an optimal scheme for tolerating double disk failures in RAID architectures', 0.769940351821)
('322398', 'The Weak Byzantine Generals Problem', '357176', 'The Byzantine Generals Problem', 0.768902617023)
('357176', 'The Byzantine Generals Problem', '322398', 'The Weak Byzantine Generals Problem', 0.768902617023)
('106729', 'Fault-tolerant distributed computing', '866137', 'ISIS: A System for Fault-Tolerant Distributed Computing', 0.763563050862)
('866137', 'ISIS: A System for Fault-Tolerant Distributed Computing', '106729', 'Fault-tolerant distributed computing', 0.763563050862)
('103729', 'Algorithms for scalable synchronization on shared-memory multiprocessors', '898438', 'Algorithms for Scalable Synchronization on Shared-Memory Multiproceessors', 0.761409234948)
('898438', 'Algorithms for Scalable Synchronization on Shared-Memory Multiproceessors', '103729', 'Algorithms for scalable synchronization on shared-memory multiprocessors', 0.761409234948)
('167487', 'Weak-consistency group communication and membership', '902626', 'WEAK-CONSISTENCY GROUP COMMUNICATION AND MEMBERSHIP (Ph.D. dissertation)', 0.761300382725)
('902626', 'WEAK-CONSISTENCY GROUP COMMUNICATION AND MEMBERSHIP (Ph.D. dissertation)', '167487', 'Weak-consistency group communication and membership', 0.761300382725)
('1895798', 'RMTP: a reliable multicast transport protocol', '2312599', 'Reliable multicast transport protocol (RMTP)', 0.759214150307)
('2312599', 'Reliable multicast transport protocol (RMTP)', '1895798', 'RMTP: a reliable multicast transport protocol', 0.759214150307)
('302436', 'Non-blocking atomic commitment', '830567', 'The Decentralized Non-Blocking Atomic Commitment Protocol', 0.746092217361)
('830567', 'The Decentralized Non-Blocking Atomic Commitment Protocol', '302436', 'Non-blocking atomic commitment', 0.746092217361)
('166609', 'Input-output performance evaluation: self-scaling benchmarks, predicted performance', '195812', 'A new approach to I/O performance evaluation: self-scaling I/O benchmarks, predicted I/O performance', 0.742809113242)
('195812', 'A new approach to I/O performance evaluation: self-scaling I/O benchmarks, predicted I/O performance', '166609', 'Input-output performance evaluation: self-scaling benchmarks, predicted performance', 0.742809113242)
('357371', 'Fail-stop processors: an approach to designing fault-tolerant computing systems', '867563', 'An Approach to Designing Fault-Tolerant Computing Systems', 0.740713866996)
('867563', 'An Approach to Designing Fault-Tolerant Computing Systems', '357371', 'Fail-stop processors: an approach to designing fault-tolerant computing systems', 0.740713866996)
('10608', 'Optimistic concurrency control for abstract data types', '308440', 'Hybrid concurrency control for abstract data types', 0.735407981636)
('308440', 'Hybrid concurrency control for abstract data types', '10608', 'Optimistic concurrency control for abstract data types', 0.735407981636)
('302436', 'Non-blocking atomic commitment', '847188', 'Reducing the cost for non-blocking in atomic commitment', 0.734543958163)
('847188', 'Reducing the cost for non-blocking in atomic commitment', '302436', 'Non-blocking atomic commitment', 0.734543958163)
('1035782', 'Byzantine quorum systems', '737912', 'Dynamic Byzantine Quorum Systems', 0.734362128447)
('258650', 'Byzantine quorum systems', '737912', 'Dynamic Byzantine Quorum Systems', 0.734362128447)
('737912', 'Dynamic Byzantine Quorum Systems', '258650', 'Byzantine quorum systems', 0.734362128447)
('737912', 'Dynamic Byzantine Quorum Systems', '1035782', 'Byzantine quorum systems', 0.734362128447)
('161724', 'TCP/IP illustrated (vol. 1): the protocols', '206746', 'TCP/IP illustrated (vol. 2): the implementation', 0.733137247372)
('206746', 'TCP/IP illustrated (vol. 2): the implementation', '161724', 'TCP/IP illustrated (vol. 1): the protocols', 0.733137247372)
('184674', 'Distributed operating systems', '549055', 'Distributed Operating Systems and Algorithms', 0.730997540363)
('549055', 'Distributed Operating Systems and Algorithms', '184674', 'Distributed operating systems', 0.730997540363)
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THis is a tinny example to show the idea of KNN for recommender development.
In [112]:
import numpy as np
from sklearn.neighbors import NearestNeighbors
# First let's create a dataset called X, with 6 records and 2 features each.
X = np.array([[-1, 2], [4, -4], [-2, 1], [-1, 3], [-3, 2], [-1, 4]])
# Next we will instantiate a nearest neighbor object, and call it nbrs. Then we will fit it to dataset X.
nbrs = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(X)
# Let's find the k-neighbors of each point in object X. To do that we call the kneighbors() function on object X.
distances, indices = nbrs.kneighbors(X)
# Let's print out the indices of neighbors for each record in object X.
indices
Out[112]:
array([[0, 3, 2],
[1, 2, 0],
[2, 4, 0],
[3, 5, 0],
[4, 2, 0],
[5, 3, 0]])
In [113]:
distances
Out[113]:
array([[ 0. , 1. , 1.41421356],
[ 0. , 7.81024968, 7.81024968],
[ 0. , 1.41421356, 1.41421356],
[ 0. , 1. , 1. ],
[ 0. , 1.41421356, 2. ],
[ 0. , 1. , 2. ]])
Imagine you have a new incoming data point. It contains the values -2 and 4. To search object X and identify the most similar record, all you need to do is call the kneighbors() function on the new incoming data p
In [114]:
print(nbrs.kneighbors([[-2, 4]]))
(array([[ 1. , 1.41421356, 2.23606798]]), array([[5, 3, 0]]))
The results indicate that the record that has neighbors with the indices [5, 3, 0] is the most similar to the new incoming data point. If you look back at the records in X, that is the last record: [-1, 4]. Just based on a quick glance you can see that, indeed, the last record in object X is the one that is most similar to this new incoming data point [-2, 4].
In [ ]:
Content source: YangDS/recommenders
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