Exploring More Realistic Evaluation Measures for Collaborative Filtering

نویسندگان

  • Giuseppe Carenini
  • Rita Sharma
چکیده

Collaborative filtering is a popular technique for recommending items to people. Several methods for collaborative filtering have been proposed in the literature and the quality of their predictions compared in empirical studies. In this paper, we argue that the measures of quality used in these studies are based on rather simple assumptions. We propose and apply additional measures for comparing the effectiveness of collaborative filtering methods which are grounded in decision-

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Intelligent Approach for Attracting Churning Customers in Banking Industry Based on Collaborative Filtering

During the last years, increased competition among banks has caused many developments in banking experiences and technology, while leading to even more churning customers due to their desire of having the best services. Therefore, it is an extremely significant issue for the banks to identify churning customers and attract them to the banking system again. In order to tackle this issue, this pa...

متن کامل

Evaluating collaborative filtering over time

Collaborative Filtering (CF) evaluation centres on accuracy: researchers validate improvements over state of the art algorithms by showing that they reduce the mean error on predicted ratings. However, this evaluation method fails to reflect the reality of deployed recommender systems, which operate algorithms that have to be iteratively updated as new users join the system and more ratings are...

متن کامل

Effect of Rating Time for Cold Start Problem in Collaborative Filtering

Cold start is one of the main challenges in recommender systems. Solving sparsechallenge of cold start users is hard. More cold start users and items are new. Sine many general methods for recommender systems has over fittingon cold start users and items, so recommendation to new users and items is important and hard duty. In this work to overcome sparse problem, we present a new method for rec...

متن کامل

یک سامانه توصیه‎گر ترکیبی با استفاده از اعتماد و خوشه‎بندی دوجهته به‎منظور افزایش کارایی پالایش‎گروهی

In the present era, the amount of information grows exponentially. So, finding the required information among the mass of information has become a major challenge. The success of e-commerce systems and online business transactions depend greatly on the effective design of products recommender mechanism. Providing high quality recommendations is important for e-commerce systems to assist users i...

متن کامل

A New Similarity Measure Based on Item Proximity and Closeness for Collaborative Filtering Recommendation

Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2004