A Comparison Evaluation of Demographic and Contextual Information of Movies using Tensor Factorization Model

doi:10.23940/ijpe.19.09.p3.23052317

Abstract

Abstract: Recommendation systems have procured massive attention due to the fast and eruptive expansion of information on the internet. Traditionally, the recommendation systems recommend products based only on the rating criteria but nowadays user expects suggestions in accordance with his requirements and might have varying preferences in different circumstances. Thus, this work presents an innovative framework to consider additional information beyond ratings that is demographic details and under what situations user interact with the system known as contextual information. This additional information is modelled as varying dimensions of the tensor factorization model. The main motive of this study is to determine the more influential dimensions among demographic and contextual dimensions and it is observed that contextual dimensions are more influential than demographic dimensions. The results validate that usage of contextual dimensions mitigates the sparsity and cold-start problems by 16% and 22% respectively in comparison to demographic information.

Key words: cold-start, contextual information, demographic information, sparsity, tensor factorization

Anu Taneja, and Anuja Arora. A Comparison Evaluation of Demographic and Contextual Information of Movies using Tensor Factorization Model [J]. Int J Performability Eng, 2019, 15(9): 2305-2317.

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