NDLI: Exploiting Linked Open Data in Cold-start Recommendations with Positive-only Feedback

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Exploiting Linked Open Data in Cold-start Recommendations with Positive-only Feedback

Content Provider	ACM Digital Library
Author	Tomeo, Paolo Fernández-Tobías, Ignacio Di Noia, Tommaso Cantador, Iván
Abstract	In recommender systems, user preferences can be acquired either explicitly by means of ratings, or implicitly --e.g., by processing text reviews, and by mining item browsing and purchasing records. Most existing collaborative filtering approaches have been designed to deal with numerical ratings, such as the 5-star ratings in Amazon and Netflix, for both rating prediction and item ranking (a.k.a. top-N recommendation) tasks. In many e-commerce and social network sites, however, user preferences are usually expressed in the form of binary and unary (positive-only) ratings, such as the thumbs up/down in YouTube and the likes in Facebook, respectively. Moreover, in these cases, the well-known problem of cold-start --i.e., the scarcity of user preferences-- is highly remarkable. To address this situation, we explore a number of graph-based and matrix factorization recommendation models that jointly exploit user ratings and item metadata. In this work, such metadata are automatically obtained from DBpedia --the queriable and structured version of Wikipedia which is considered as the core knowledge repository of the Linked Open Data initiative--, and the models are evaluated with a Facebook dataset covering three distinct domains, namely books, movies and music. The results achieved in our experiments show that the proposed hybrid recommendation models, which exploit rating and semantic data, outperform content-based and collaborative filtering baselines.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450341417
DOI	10.1145/2934732.2934745
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-06-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Linked data Dbpedia Positive-only feedback Hybrid recommendation Facebook Recommender systems Cold-start
Content Type	Text
Resource Type	Article

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

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