User Friendly Recommender Systems

Recommender systems are a recent but increasingly widely used resource. Yet most, if not all of them suffer from serious deficiencies. Recommender systems often require first time users to enter ratings for a large number of items — a tedious process that often deters users. Thus, this thesis investigated whether useful recommendations could be made without requiring users to explicitly rate items. It was shown that ratings automatically generated from implicit information about a user can be used to make useful recommendations. Most recommender systems also provide no explanations for the recommendations that they make, and give users little control over the recommendation process. Thus, when these systems make a poor recommendation, users can not understand why it was made, and are not able to easily improve their recommendations. Hence, this thesis investigated ways in which scrutability and control could be implemented in such systems. A comprehensive questionnaire was completed by 18 participants as a basis for a broader understanding of the issues mentioned above and to inform the design of a prototype; a prototype was then created and two separate evaluations performed, each with at least 9 participants. This investigation highlighted a number of key scrutability and control features that could be useful additions to existing recommender systems. The findings of this thesis can be used to improve the effectiveness, usefulness and user friendliness of existing recommender systems. These findings include: • Explanations, controls and a map based presentation are all useful additions to a recommender system. • Specific explanation types can be more useful than others for explaining particular recommendation techniques. • Specific recommendation techniques can be useful even when a user has not entered many ratings. • Ratings generated from purely implicit information about a user can be used to made useful recommendations.