NDLI: Relevance Meets Coverage: A Unified Framework to Generate Diversified Recommendations

Content Provider	ACM Digital Library
Author	Guo, Guangming Wu, Le Yuan, Nicholas Jing Xie, Xing Chen, Enhong Liu, Qi
Copyright Year	2016
Abstract	Collaborative filtering (CF) models offer users personalized recommendations by measuring the $\textit{relevance}$ between the active user and each individual candidate item. Following this idea, user-based collaborative filtering (UCF) usually selects the local popular items from the like-minded neighbor users. However, these traditional relevance-based models only consider the individuals (i.e., each neighbor user and candidate item) separately during neighbor set selection and recommendation set generation, thus usually incurring highly similar recommendations that lack diversity. While many researchers have recognized the importance of diversified recommendations, the proposed solutions either needed additional semantic information of items or decreased accuracy in this process. In this article, we describe how to generate both accurate and diversified recommendations from a new perspective. Along this line, we first introduce a simple measure of $\textit{coverage}$ that quantifies the usefulness of the whole set, that is, the neighbor userset and the recommended itemset as a complete entity. Then we propose a recommendation framework named REC that considers both traditional relevance-based scores and the new coverage measure based on UCF. Under REC, we further prove that the goals of maximizing relevance and coverage measures simultaneously in both the neighbor set selection step and the recommendation set generation step are NP-hard. Luckily, we can solve them effectively and efficiently by exploiting the inherent submodular property. Furthermore, we generalize the coverage notion and the REC framework from both a data perspective and an algorithm perspective. Finally, extensive experimental results on three real-world datasets show that the REC-based recommendation models can naturally generate more diversified recommendations without decreasing accuracy compared to some state-of-the-art models.
Starting Page	1
Ending Page	30
Page Count	30
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/2700496
Volume Number	7
Issue Number	3
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-02-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Collaborative filtering Coverage Diversity Personalized recommendation
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-N Recommendations

Beyond Relevance: Explicitly Promoting Novelty and Diversity in Tag Recommendation

Learning travel recommendations from user-generated GPS traces

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

Nontrivial landmark recommendation using geotagged photos

Unified relevance models for rating prediction in collaborative filtering

Anytime Algorithms for Recommendation Service Providers

Object-Oriented Travel Package Recommendation

VSRank: A Novel Framework for Ranking-Based Collaborative Filtering

Relevance Meets Coverage: A Unified Framework to Generate Diversified Recommendations

Similar Documents

A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-N Recommendations

Beyond Relevance: Explicitly Promoting Novelty and Diversity in Tag Recommendation

Learning travel recommendations from user-generated GPS traces

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

Nontrivial landmark recommendation using geotagged photos

Unified relevance models for rating prediction in collaborative filtering

Anytime Algorithms for Recommendation Service Providers

Object-Oriented Travel Package Recommendation

VSRank: A Novel Framework for Ranking-Based Collaborative Filtering

Relevance Meets Coverage: A Unified Framework to Generate Diversified Recommendations