A Context Features Selecting and Weighting Methods for Context-Aware Recommendation

TitleA Context Features Selecting and Weighting Methods for Context-Aware Recommendation
Publication TypeConference Paper
Year of Publication2015
AuthorsZAMMALI, S, Arour, K, BOUZEGHOUB, A
Conference NameInternational Computer Software and Applications Conference (COMPSAC)
Date PublishedJuly
Conference LocationTaichung, Taiwan
Abstract

The notion of “Context” plays a key role in recommender systems. In this respect, many researches have beendedicated for Context-Aware Recommender Systems (CARS).Rating prediction in CARS is being tackled by researchersattempting to recommend appropriate items to users. However,in rating prediction, three thriving challenges still to tackle:(i)context feature’s selection; (ii) context feature’s weighting; and(iii) users context matching. Context-aware algorithms made astrong assumption that context features are selected in advanceand their weights are the same or initialized with randomvalues. After context features weighting, users context matchingis required. In current approaches, syntactic measures are usedwhich require an exact matching between features. To addressthese issues, we propose a novel approach for Selecting andWeighting Context Features (SWCF). The evaluation experimentsshow that the proposed approach is helpful to improve therecommendation quality.

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