Serendipitous Recommendation in E-Commerce Using Innovator – Based Collaborative Filtering

Abstract

Serendipitous Recommendation in E-Commerce Using Innovator – Based Collaborative Filtering (CF) java project report algorithms have been widely used to build recommender systems since they have distinguishing capability of sharing collective wisdoms and experiences. However, they may easily fall into the trap of the Matthew effect, which tends to recommend popular items and hence less popular items become increasingly less popular. Under this circumstance, most of the items in the recommendation list are already familiar to users and therefore the performance would seriously degenerate in finding cold items, i.e., new items and niche items.

To address this issue, in this paper, a user survey is first conducted on the online shopping habits in China, based on which a novel recommendation algorithm termed innovator based CF is proposed that can recommend cold items to users by introducing the concept of innovators. onfiguration:

H/W System Configuration:-

System          : Pentium I3 Processor.
Hard Disk       : 500 GB.
Monitor          : Standard LED Monitor
Input Devices : Keyboard
Ram               : 4 GB

S/W System Configuration:-

Operating system              : Windows 7/8/10.
Available Coding Language : Java and Phonegap
Database                          : MYSQL

Conclusion 

In Serendipitous Recommendation in E-Commerce Using Innovator – Based Collaborative Filtering java project report paper, we have addressed the serendipity issue of recommendation by developing a novel recommendation method termed INVBCF based on the user survey on the online shopping habits in China. In particular, by introducing the concept of innovators, new items and niche items can be introduced into the recommendation list and hence achieving the balance between serendipity and accuracy.

For efficiently catching the needs of users accurately in real time, an offline component and an online component have been designed, which also saves communication cost and computing resources. The experimental results show that our proposed method beats other methods on serendipity while maintaining good performance on accuracy, novelty and coverage as well. In our future work, we plan to investigate the social structure in discovering innovators provided that the social structure is given.