An Integrated Recommender System Using Semantic Web With Social Tagging System

An Integrated Recommender System Using Semantic Web With Social Tagging System

R. Indra (Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India) and Muthuraman Thangaraj (Madurai Kamaraj University, Madurai, India)
Copyright: © 2019 |Pages: 21
DOI: 10.4018/IJSWIS.2019040103

Abstract

Social tagging systems (STSs) allow collaborative users to share and annotate many types of resources with descriptive and semantically meaningful information in freely chosen text labels. STS provides three recommendations such as tag, item and user recommendations. Existing recommendation algorithms transform the three dimensional space of user, resource, and tag into two dimensions using pair relations in order to apply existing techniques. However, users may have different interests for an item, and items may have multiple facets. To circumvent this, a new system that models three types of entities user, tag and item in a STS as a 3-order tensor is proposed. The sparsity is reduced using stemming and predictions are made by applying latent semantic indexing using randomized singular value decomposition (RSVD). The proposal provides all the three recommendations using semantic web and shows notable improvements in terms of effectiveness through indices such as recall, precision, time and space.
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Introduction

STSs are web applications in which users upload resources (e.g., scientific publications, music, bookmarks, photos, etc.) with annotations from a list of freely chosen keywords called tags. For example, STSs such as Delicious1, BibSonomy2, Flickr3, Last.fm4, etc., bring people together through their shared interests. Although the primary goal of tags is to help individual users to organize and retrieve their own content, the exposition of tags by the system ends up benefiting other users since they can adopt each other’s tags for browsing and annotating resources. With the increase of tagging activity, a lightweight collaborative classification system, typically known as Folksonomy5 emerges. Folksonomy is an aggregation of tags for multiple resources, shared by multiple users.

With the extensive popularity of these systems and the increasing amount of user contributed content, information overload becomes an issue. This issue is handled by Recommender Systems which increases the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. Singh et al. (2017) presented a survey on the generation of recommender systems and FadhelAljunid and Manjaiah (2017) presented a survey on recommendation systems for social media using Big Data analytics. In social tagging systems there are three possible modes of recommendation: tags, resources and users. Therefore, suitable methods are required to exploit the different dimensions of social tagging systems to provide recommendations.

In Tag recommendation tags are recommended at the time when a user wants to annotate a resource. Tag recommendation is useful in simplifying the tagging process for users to find good tags and consolidate the tag vocabulary across users because most tags in STSs are uncontrolled, redundant and ambiguous. Existing tag recommendation algorithms (Jäschke et al., 2008; Xu et al., 2012; Djuana et al., 2014; Jha & Goyal, 2014; Alepidou, Zinovia, Konstantinos, Vavliakis, & Mitkas, 2011) in STSs considers only two dimensions of STSs for making tag recommendations. The study on the influence of frequency, recency and semantic context on the reuse of tags is given by (Kowald & Lex, 2016) which will be useful in tag recommendation.

Resource recommender systems are useful for users to provide personalized recommendations for information, products or services during a live interaction. Recommendations can be based on demographics of the users, overall top selling items or past buying habit of users as a future predictor for future items (Sarwar, et al., 2001). Resource recommendation systems (Parra & Brusilovsky, 2009; Htun & Tar, 2014; Anand & Mampilli, 2014; Zhang, Zi-Ke, Zhou & Zhang, 2011; Rafailidis, Dimitrios, & Daras, 2013; Misaghian, Jalali & Moattar, 2013; Zhang et al., 2014) provide only recommendations for items. The words resource and item are used interchangeably throughout the paper.

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