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@article{144111,
        author = {Priya Agrawal and Tejas Kadiya and Ramesh Prajapati},
        title = {A Comparative Survey of Collaborative Filtering Similarity Measures: Limitations of Current Similarity and Formalization of New Similarity Measure},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {3},
        number = {6},
        pages = {94-97},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=144111},
        abstract = {The technique of Collaborative Filtering is especially successful in generating personalized recommendations. Collaborative Filtering is quickly becoming a popular technique for reducing information overload, often as a technique to complement content based information filtering. More than a decade of research has resulted in numerous algorithms, although no comparison of the different strategies has been made. In fact, a universally accepted way of evaluating a Collaborative Filtering algorithm does not exist yet. In this survey, we explain different techniques found in the literature, and we study the characteristics of each one, highlighting their principal strengths and weaknesses. This Paper Present a new user similarity model to improve the recommendation performance to calculate the similarity of each user. The model not only consider the local context information of user rating but also the global preferences of user behavior.},
        keywords = { PCC, Jaccard, MSD, cold-start, Proximity, Sigmoid  },
        month = {},
        }