CNR: cross-network recommendation embedding user’s personality

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

Abstract

With the explosive growth of available data, recommender systems have become an essential tool to ease users with their decision-making procedure. One of the most challenging problems in these systems is the data sparsity problem, i.e., lack of sufficient amount of available users’ interactions data. Recently, cross-network recommender systems with the idea of integrating users’ activities from multiple domain were presented as a successful solution to address this problem. However, most of the existing approaches utilize users’ past behaviour to discover users’ preferences on items’ patterns and then suggest similar items to them in the future. Hence, their performance may be limited due to ignore recommending divers items. Users are more willing to be recommended with a variety set of items not similar to those they preferred before. Therefore, diversity plays a crucial role to evaluate the recommendation quality. For instance, users who used to watch comedy movie, may be less likely to receive thriller movie, leading to redundant type of items and decreasing user’s satisfaction. In this paper, we aim to exploit user’s personality type and incorporate it as a primary and enduring domain-independent factor which has a strong correlation with user’s preferences. We present a novel technique and an algorithm to capture users’ personality type implicitly without getting users’ feedback (e.g., filling questionnaires). We integrate this factor into matrix factorization model and demonstrate the effectiveness of our approach, using a real-world dataset.

LanguageEnglish
Title of host publicationData Quality and Trust in Big Data
Subtitle of host publication5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers
EditorsHakim Hacid, Quan Z. Sheng, Tetsuya Yoshida, Azadeh Sarkheyli, Rui Zhou
PublisherSpringer-VDI-Verlag GmbH & Co. KG
Pages62-77
Number of pages16
ISBN (Electronic)9783030191436
ISBN (Print)9783030191429
DOIs
Publication statusPublished - 1 Jan 2019
Event5th International Workshop on Data Quality and Trust in Big Data, QUAT 2018, held in conjunction with the International Conference on Web Information Systems Engineering, WISE 2018 - Dubai, United Arab Emirates
Duration: 12 Nov 201815 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11235 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Workshop on Data Quality and Trust in Big Data, QUAT 2018, held in conjunction with the International Conference on Web Information Systems Engineering, WISE 2018
CountryUnited Arab Emirates
CityDubai
Period12/11/1815/11/18

Fingerprint

Recommender systems
Recommendations
Factorization
Decision making
Feedback
Recommender Systems
Personality
Matrix Factorization
User Preferences
User Interaction
Sparsity
Questionnaire
Decision Making
Likely
Integrate
Sufficient
Evaluate

Cite this

Yakhchi, S., Ghafari, S. M., & Beheshti, A. (2019). CNR: cross-network recommendation embedding user’s personality. In H. Hacid, Q. Z. Sheng, T. Yoshida, A. Sarkheyli, & R. Zhou (Eds.), Data Quality and Trust in Big Data: 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers (pp. 62-77). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11235 LNCS). Springer-VDI-Verlag GmbH & Co. KG. https://doi.org/10.1007/978-3-030-19143-6_5
Yakhchi, Shahpar ; Ghafari, Seyed Mohssen ; Beheshti, Amin. / CNR : cross-network recommendation embedding user’s personality. Data Quality and Trust in Big Data: 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers. editor / Hakim Hacid ; Quan Z. Sheng ; Tetsuya Yoshida ; Azadeh Sarkheyli ; Rui Zhou. Springer-VDI-Verlag GmbH & Co. KG, 2019. pp. 62-77 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{2fd9f3101afc46358ffc81c7de769a29,
title = "CNR: cross-network recommendation embedding user’s personality",
abstract = "With the explosive growth of available data, recommender systems have become an essential tool to ease users with their decision-making procedure. One of the most challenging problems in these systems is the data sparsity problem, i.e., lack of sufficient amount of available users’ interactions data. Recently, cross-network recommender systems with the idea of integrating users’ activities from multiple domain were presented as a successful solution to address this problem. However, most of the existing approaches utilize users’ past behaviour to discover users’ preferences on items’ patterns and then suggest similar items to them in the future. Hence, their performance may be limited due to ignore recommending divers items. Users are more willing to be recommended with a variety set of items not similar to those they preferred before. Therefore, diversity plays a crucial role to evaluate the recommendation quality. For instance, users who used to watch comedy movie, may be less likely to receive thriller movie, leading to redundant type of items and decreasing user’s satisfaction. In this paper, we aim to exploit user’s personality type and incorporate it as a primary and enduring domain-independent factor which has a strong correlation with user’s preferences. We present a novel technique and an algorithm to capture users’ personality type implicitly without getting users’ feedback (e.g., filling questionnaires). We integrate this factor into matrix factorization model and demonstrate the effectiveness of our approach, using a real-world dataset.",
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Yakhchi, S, Ghafari, SM & Beheshti, A 2019, CNR: cross-network recommendation embedding user’s personality. in H Hacid, QZ Sheng, T Yoshida, A Sarkheyli & R Zhou (eds), Data Quality and Trust in Big Data: 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11235 LNCS, Springer-VDI-Verlag GmbH & Co. KG, pp. 62-77, 5th International Workshop on Data Quality and Trust in Big Data, QUAT 2018, held in conjunction with the International Conference on Web Information Systems Engineering, WISE 2018, Dubai, United Arab Emirates, 12/11/18. https://doi.org/10.1007/978-3-030-19143-6_5

CNR : cross-network recommendation embedding user’s personality. / Yakhchi, Shahpar; Ghafari, Seyed Mohssen; Beheshti, Amin.

Data Quality and Trust in Big Data: 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers. ed. / Hakim Hacid; Quan Z. Sheng; Tetsuya Yoshida; Azadeh Sarkheyli; Rui Zhou. Springer-VDI-Verlag GmbH & Co. KG, 2019. p. 62-77 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11235 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

TY - GEN

T1 - CNR

T2 - cross-network recommendation embedding user’s personality

AU - Yakhchi,Shahpar

AU - Ghafari,Seyed Mohssen

AU - Beheshti,Amin

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KW - Recommender system

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Yakhchi S, Ghafari SM, Beheshti A. CNR: cross-network recommendation embedding user’s personality. In Hacid H, Sheng QZ, Yoshida T, Sarkheyli A, Zhou R, editors, Data Quality and Trust in Big Data: 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Revised Selected Papers. Springer-VDI-Verlag GmbH & Co. KG. 2019. p. 62-77. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-19143-6_5