CDRec-CAS

Recommender Systems (RSs) are a subclass of information filtering systems. RSs assist users in choosing interesting items from an extensive collection of items. This article addresses two research topics in RS, namely cross-domain RSs (CDRSs) and the context-aware RSs (CARSs). CDRSs were developed to improve the quality of recommendations in a target domain using the source domain information. Moreover, CDRSs look to limit the spread of fake information through RSs. CARSs are designed to utilize contextual information, such as location, time, companions, and others, in the recommendation as user interests change with context. In this work, CDRSs and CARSs are implemented in an integrated manner to construct a more specific RS that offers both these systems’ advantages. For including contextual information in data, contextual prefiltering is applied. These approaches recommend items more accurately, overcoming cold start, sparsity, and scalability issues, and provide a more personalized, novel, and diversified recommendation. The developed system, cross-domain recommendation using context-aware sequences (CDRec-CAS), is evaluated in terms of accuracy achieved in recommending preferred item sequences and the next preferred item. In recommending preferred item sequences, it is found that it improves recommendation accuracy that varied from approximately $7.85$ %– $9.74$ % (considering the single context) and $4.41$ %– $8.17$ % (considering dual-context) when compared with existing noncontextual RS. In recommending the next preferred item, it is found that it improves recommendation accuracy that varied from approximately $3.81$ %– $9.81$ % (considering the single context) $-$ $2.24$ %– $9.21$ % (considering dual-context) when compared with existing noncontextual RS. The results obtained by implementing CDRec-CAS are compared with existing approaches, proving that recommendations can be enhanced using cross-domain and contextual information

Geography · Computer Science · Data Management and Algorithms · Mathematics · Recommender Systems and Techniques · Video Analysis and Summarization

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