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Fuzzy logic based similarity measure for multimedia contents recommendation

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Abstract

Collaborative filtering is one of the mainstream approaches to provide recommendations in various online environments such as Ecommerce. Although this is a popular method for service recommendation, it still suffers from sparsity issue where only a small number of rating records are available for some new items or users in the system. Consequently, the accuracy of rate prediction is often compromised. Unlike the conventional collaborative filtering methods that directly compute the similarity between users, this paper presents a fuzzy logic based approach to refine the similarity obtained using traditional approaches like Pearson correlation, Cosine, Adjusted Cosine etc. Experiments were conducted on the two popular benchmark datasets and it shows that the proposed method obtains better prediction accuracy as compare to other traditional similarity measures.

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Abbreviations

S :

is the set of users

U, U ' :

Some users

P, P ' :

Some items

r U,  P :

The rating of user U on item P

\( {\overline{r}}_U \) :

Mean rating value for user U

\( {\overline{r}}_P \) :

Mean rating value for item P

I :

is the set of items

μP :

μ P is the average rating of item P

r i :

is the true rating.

Pred i :

is the vote predicted for a movie

|S|:

is the cardinality of the test ratings

|I U |:

is the cardinality of item rated by user U.

|N|:

is the number of items co-rated by two users

r med :

is the median value in rating scale

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Authors and Affiliations

  1. Department of Polymer and Process Engineering, Indian Institute of Technology, Roorkee, 247667, India

    Surya Kant & Tripti Mahara

  2. Department of Computer Science & Engineering, JUET, Raghogarh, M.P., India

    Vinay Kumar Jain

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Deepak Kumar Jain

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  1. Surya Kant
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  4. Deepak Kumar Jain
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Correspondence to Surya Kant.

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Kant, S., Mahara, T., Jain, V.K. et al. Fuzzy logic based similarity measure for multimedia contents recommendation. Multimed Tools Appl 78, 4107–4130 (2019). https://doi.org/10.1007/s11042-017-5260-2

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