Most Influential Contextual-Features [MICF] based model for Context-Aware Recommender System

Satakshi Rana, Arpit Jain, V. K. Panchal

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

Recommender system is an information filtering system that finds its applications in various e-commerce related fields. It recommends a list of items to an end-user from a potentially overwhelming collection of choices. Since the preferences of a user is different from the likings of other users, traditional recommender systems that recommend toprated entities to all the users, may not suffice in anticipating the needs of a user. Therefore, contextualization of recommender system is required to act more efficiently and in a user-specific manner. In an effort to deliver personalized recommendations shaped by user's contextual information, we have devised a novel methodology to incorporate contextual information into the recommender system. The proposed algorithm presents a framework for identifying the relevant contextual-variables and generating the cluster of contextual-features that depict similar rating-pattern for each class of entities. Thereafter, determining the set of Most Influential Contextual-Features that exhibit same rating-pattern as the end-user across all classes and predict the rating an end-user will give to an item, he has not rated before. Our algorithm not only renders intelligent and personalized recommendations but also alleviates cold-start, sparsity and newitem problem of traditional recommender system.

Original languageEnglish
DOIs
Publication statusPublished - 01-01-2013
Externally publishedYes
Event2013 IEEE International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, IEEE-C2SPCA 2013 - Bangalore, India
Duration: 10-10-201311-10-2013

Conference

Conference2013 IEEE International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, IEEE-C2SPCA 2013
CountryIndia
CityBangalore
Period10-10-1311-10-13

Fingerprint

Recommender systems
Information filtering

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Signal Processing

Cite this

Rana, S., Jain, A., & Panchal, V. K. (2013). Most Influential Contextual-Features [MICF] based model for Context-Aware Recommender System. Paper presented at 2013 IEEE International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, IEEE-C2SPCA 2013, Bangalore, India. https://doi.org/10.1109/C2SPCA.2013.6749418
Rana, Satakshi ; Jain, Arpit ; Panchal, V. K. / Most Influential Contextual-Features [MICF] based model for Context-Aware Recommender System. Paper presented at 2013 IEEE International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, IEEE-C2SPCA 2013, Bangalore, India.
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Rana, S, Jain, A & Panchal, VK 2013, 'Most Influential Contextual-Features [MICF] based model for Context-Aware Recommender System' Paper presented at 2013 IEEE International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, IEEE-C2SPCA 2013, Bangalore, India, 10-10-13 - 11-10-13, . https://doi.org/10.1109/C2SPCA.2013.6749418

Most Influential Contextual-Features [MICF] based model for Context-Aware Recommender System. / Rana, Satakshi; Jain, Arpit; Panchal, V. K.

2013. Paper presented at 2013 IEEE International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, IEEE-C2SPCA 2013, Bangalore, India.

Research output: Contribution to conferencePaper

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Rana S, Jain A, Panchal VK. Most Influential Contextual-Features [MICF] based model for Context-Aware Recommender System. 2013. Paper presented at 2013 IEEE International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, IEEE-C2SPCA 2013, Bangalore, India. https://doi.org/10.1109/C2SPCA.2013.6749418