ABSTRACT Many factors that influence users' decision making processes in Recommender Systems (RSs... more ABSTRACT Many factors that influence users' decision making processes in Recommender Systems (RSs) have been investigated by a relatively vast research of empirical and theoretical nature, mostly in the field of e-commerce. In this paper, we discuss some aspects of the user experience with RSs that may affect the decision making process and outcome, and have been marginally addressed by prior research.
Abstract Most recommender systems work on single domains, ie, they recommend items related to the... more Abstract Most recommender systems work on single domains, ie, they recommend items related to the same domain where users have expressed ratings. However, the integration of different domains into one recommender system could allow users to span over different types of items. For instance, users that have watched live TV programs could like to be recommended with on-demand movies, music, mobile applications, friends to connect to, etc. This paper focuses on cross-domain collaborative recommender systems, whose aim ...
ABSTRACT In many commercial systems, the 'best bet&a... more ABSTRACT In many commercial systems, the 'best bet'recommendations are shown, but the predicted rating values are not. This is usually referred to as a top-N recommendation task, where the goal of the recommender system is to find a few specific items which are supposed to be most appealing to the user. Common methodologies based on error metrics (such as RMSE) are not a natural fit for evaluating the top-N recommendation task. Rather, top-N performance can be directly measured by alternative methodologies based on ...
Proceedings of the 8th international …, Jan 1, 2010
ABSTRACT In this paper we evaluate the performance of different collaborative filtering algorithm... more ABSTRACT In this paper we evaluate the performance of different collaborative filtering algorithms over time, where new users, new items, and new ratings are constantly added to the recommender dataset. The analysis has been performed on the datasets collected by two IPTV providers. Both datasets have been implicitly collected by analyzing the pay-per-view movies purchased by the users over a period of several months. The first result of the paper outlines that item-based algorithms perform better with respect to SVD-based ones in the early stage of the cold-start ...
ABSTRACT Many factors that influence users' decision making processes in Recommender Systems (RSs... more ABSTRACT Many factors that influence users' decision making processes in Recommender Systems (RSs) have been investigated by a relatively vast research of empirical and theoretical nature, mostly in the field of e-commerce. In this paper, we discuss some aspects of the user experience with RSs that may affect the decision making process and outcome, and have been marginally addressed by prior research.
Abstract Most recommender systems work on single domains, ie, they recommend items related to the... more Abstract Most recommender systems work on single domains, ie, they recommend items related to the same domain where users have expressed ratings. However, the integration of different domains into one recommender system could allow users to span over different types of items. For instance, users that have watched live TV programs could like to be recommended with on-demand movies, music, mobile applications, friends to connect to, etc. This paper focuses on cross-domain collaborative recommender systems, whose aim ...
ABSTRACT In many commercial systems, the 'best bet&a... more ABSTRACT In many commercial systems, the 'best bet'recommendations are shown, but the predicted rating values are not. This is usually referred to as a top-N recommendation task, where the goal of the recommender system is to find a few specific items which are supposed to be most appealing to the user. Common methodologies based on error metrics (such as RMSE) are not a natural fit for evaluating the top-N recommendation task. Rather, top-N performance can be directly measured by alternative methodologies based on ...
Proceedings of the 8th international …, Jan 1, 2010
ABSTRACT In this paper we evaluate the performance of different collaborative filtering algorithm... more ABSTRACT In this paper we evaluate the performance of different collaborative filtering algorithms over time, where new users, new items, and new ratings are constantly added to the recommender dataset. The analysis has been performed on the datasets collected by two IPTV providers. Both datasets have been implicitly collected by analyzing the pay-per-view movies purchased by the users over a period of several months. The first result of the paper outlines that item-based algorithms perform better with respect to SVD-based ones in the early stage of the cold-start ...
Uploads