NDLI: Using lower-bound similarity to enhance the performance of recommender systems

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Using lower-bound similarity to enhance the performance of recommender systems

Content Provider	ACM Digital Library
Author	Reddy, P. Krishna Sharma, Mohak
Abstract	Recommender systems employ the popular K-nearest neighbor collaborative filtering (K-CF) methodology and its variations for recommending the products. In K-CF approach, recommendation for a given user is computed based on the ratings of K-nearest neighbors. In K-CF approach, it can be noted that, the system identifies K neighbors for each user irrespective of the number of products he/she has rated. As a result, the user who have rated few products may get the less-similar neighbors and the user who have rated more products may miss the genuine neighbors. In the literature, the notion of lower-bound similarity has been proposed to improve the clustering performance in which the clusters are extracted by fixing the similarity threshold. In this paper, we have extended the notion of lower-bound similarity to recommender systems to improve the performance of K-CF approach. In the proposed approach, instead of fixing K for finding the neighborhood, the similarity threshold value is fixed to extract the neighbors for each user. As a result, each user gets appropriate number of neighbors based on the number of products rated by him/her in a dynamic manner. The experimental results, on MovieLens dataset, show that the proposed lower bound similarity CF approach improves the performance of recommender systems over K-CF approach.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450307505
DOI	10.1145/1980422.1980444
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-03-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Lower-bound similarity Collaborative filtering K-nearest neighbor Recommender systems
Content Type	Text
Resource Type	Article

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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
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