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The explosive growth of information asks for advanced information filtering techniques to solve the so-called information overload problem. A promising way is the recommender system which analyzes the historical records of users’ activities and accordingly provides personalized recommendations. Most recommender systems can be represented by user-object bipartite networks where users can evaluate and...
In most recommender systems, the active user's preferences can be denoted by multi-graded rating data (1 to 5 in MovieLens etc.). When using the available ratings, some recommendation algorithms transfer multi-graded rating data into binary rating data ignoring the actual value of ratings, while some others just use positive ratings (no smaller than 3 in 1-5 rating structure e.g.) to recommend items...
Recommender systems have made significant progress over the last decade and several industrial-strength systems have been developed. Typically, recommender systems try to predict people's preferences and use accuracy indices such as mean absolute error to judge the performance of the algorithms. Recently, the diversity index is widely accepted as another metric. However, the ability of a recommendation...
For any product recommendation systems, the most important thing is to improve the accuracy of prediction of customer preferences on products. If there is not enough information of a product, especially when a new product is introduced into the system, it is difficult to recommend the product to other customers. If we can select few customers to rate this product we may predict more accurate. We term...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.