Abstract
In the context of big data, a recommendation system has been put forth as an efficient strategy for predicting the consumer’s pref-erences while rating items. Organizations that are functioning with multiple branches are in the imperative need for analyzing their multi-source big data to arrive novel decisions with respect to branch level and central level. In such circumstances, a multi-state business organi-zation would like to analyze their consumer preferences and enhance their decision-making activities based on the taste/preferences obtained from diversified data sources located in different places. One of the problems in current Item-based collaborative filtering approach is that users and their ratings have been considered uniformly while recording their preferences about target items. To improve the quality of rec-ommendations, the paper proposes various weighting strategies for arriving effective recommendation of items especially when the sources of data are multi-source in nature. For a multi-source data environment, the proposed strategies would be effective for validating the active user rating for a target item. To validate the novelty of the proposal, a Hadoop based big data eco-system with aid of Mahout has been con-structed and experimental investigations are carried out in a benchmark dataset.
Highlights
With the recent advancement of data generation strategies and tools, voluminous amount of data has been generated from multiple data source which are characterized by heterogeneity, velocity and deemed as “Big Data”
From the perspective of E-commerce, recommender system has been considered as a tool to help user’s search based on their interest and preferences [1].There are various approaches which have been built on the concept of recommendation system and they are (i) Collaborative Filtering – It performs recommendations by identifying other users with similar taste and uses their opinion to recommend items to the active user. (ii) Content-based Filtering – It normally performs prediction based on the user’s information and ignores the contribution from other users as with the case of collaborative filtering (iii) Hybrid Filtering -which combines two or more filtering techniques in different ways in order to increase the accuracy and performance of recommender systems
With the advent of emerging web repositories and big data of multisourced nature, organizations are in imperative need for analyzing their user preferences/taste in an effective manner
Summary
With the recent advancement of data generation strategies and tools, voluminous amount of data has been generated from multiple data source which are characterized by heterogeneity, velocity and deemed as “Big Data”. When the techniques listed in the above taxonomy are applied in the context of multi-source big data, novel user preferences on the basis of individual branches would be reaped to personalize the user preferences in an effective manner. In such circumstances, adaptation of weighting model for effective recommendation would emerge as an interesting research problem. We have focussed item-based collaborative filtering technique in a multisource big data context for predicting effective preferences of an active user by considering various weighting parameters. The importance of considering various weighting parameters are elaborately discussed in the Section 4 and Section 5 concludes the paper
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