Information theoretic approach for cold start users with diversity improvement technique for semantic recommender system

被引：0

作者：

Kushwaha, Nidhi ^{[1
]}

Vyas, O.P. ^{[1
]}

机构：

[1] Indian Institute of Information Technology, Allahabad, Allahabad, 211012, U.P.

来源：

International Journal of Metadata, Semantics and Ontologies | 2015年 / 10卷 / 04期

关键词：

Diversity; Global diversity; Information theory; Intra list diversity; Popularity; Recommender system; Semantic web;

D O I：

10.1504/IJMSO.2015.074753

中图分类号：

学科分类号：

摘要：

This paper aims to use semantic database for recommendation purposes. It deals with two very specific problems of Recommendation System, namely Cold Start user and Diversity. We first describe cold start users and predict recommendation for them using information theory based methods. To introduce serendipitous results we also include aggregate diversity methods to the predicted ratings. Furthermore, we explain the results obtained from the rated items, and also increase the Intra List Diversity using a ranking-based approach that is different from the popularity-based approach employed in the past. Copyright © 2015 Inderscience Enterprises Ltd.

引用

页码：281 / 290

页数：9

共 23 条

[1]

Adomavicius G., Kwon Y., Improving recommendation diversity using ranking-based techniques, IEEE Transactions on Knowledge and Data Engineering, 24, 5, (2012)

[2]

Adomavicius G., Tuzhilin A., Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, 17, 6, (2005)

[3]

Ampazis N., Emmanouilidis T., Exploring semantic features for producing top-N recommendation lists from Binary User Feedback, ESWC 2014, (2014)

[4]

Bizer C., Seaborne A., D2RQ - Treating non-RDF databases as virtual RDF graphs, ISWC'2004, (2004)

[5]

Bizer C., Heath T., Berners-Lee T., Linked data - The story so far, International Journal on Semantic Web and Information Systems, 5, 3, pp. 1-22, (2009)

[6]

Campbell L.M., MacNeill S., The Semantic Web, Linked and Open Data, (2010)

[7]

Erling O., Mikhailov I., RDF support in the virtuoso DBMS, Studies in Computational Intelligence, 221, pp. 7-24, (2009)

[8]

Florez O., Nachman L., Deep learning of semantic word representations to implement a content-based recommender for the RecSys challenge, ESWC 2014, (2014)

[9]

Hurley N., Zhang M., Novelty and diversity in top-N recommendation - Analysis and evaluation, ACM Transactions on Internet Technology, 10, 4, (2011)

[10]

Kushwaha N., Vyas O.P., SemMovieRec: Extraction of semantic features of DBpedia for Recommender System, Proceedings of the 7th ACM India Computing Conference (COMPUTE'14), pp. 1-9, (2014)

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