A Propound Hybrid Approach for Personalized Online Product Recommendations

The main aim of e-commerce websites is to turn their visitors into customers. For this purpose, recommender system is used as a tool that helps in turning clicks into purchases. Obtaining explicit ratings often faces problems such as authenticity of the ratings given by customers and queries that leads to low accuracy of the recommendations. Implicit ratings play a vital role in providing refined ranking of products. Preference level of the customers are predicted based on collaborative filtering (CF) approach using implicit details and mining click stream paths of like-minded users. Extracting the similarity among products using sequential patterns improves the accuracy of ranking. Integrating these two approaches improves the recommendation quality. Based on the results of experiment carried out to compare the performance of CF, sequential path of products viewed and integration of the two, we conclude that integration of mentioned approaches is superior to the existing ones.