  This paper proposes a number of explicit and implicit ratings in product
recommendation system for Business-to-customer e-commerce purposes. The system
recommends the products to a new user. It depends on the purchase pattern of
previous users whose purchase pattern is close to that of a user who asks for a
recommendation. The system is based on weighted cosine similarity measure to
find out the closest user profile among the profiles of all users in database.
It also implements Association rule mining rule in recommending the products.
Also, this product recommendation system takes into consideration the time of
transaction of purchasing the items, thus eliminating sequence recognition
problem. Experimental result shows for implicit rating, the proposed method
gives acceptable performance in recommending the products. It also shows
introduction of association rule improves the performance measure of
recommendation system.
