Knowing me knowing you'-using profiles and social networking to improve recommender systems such domains and which recommender characteristics HCI approaches to rs research have been limited to are important to them examining existing rss in order to establish interaction Human-computer interaction(HCi)approaches RS research have primarily focused on evaluating the When comparing how advice from friends and from user interface of existing RSs [3-5] RS is perceived, Swearingen and Sinha [4, 9] found that while people overall preferred recommendations from Our research, however took a step back and asked their friends, they appreciated the ability of an rs to when users seek recommendations, and what provide serendipitous recommendations that information they hope to receive. We translated our broadened their horizons. In that context, Swearingen findings into a mock-up design of an RS which and Sinha (5, 9] identified two factors as fundamentally incorporates social networking features, and then important in the overall usefulness of an RS-familiar valuated those designs through experiments. This recommendations and system transparency paper first presents a brief review of rs research to date followed by an overview of the psychology Familiar recommendations can come in different literature on advice-seeking and decision making We forms items previously consumed or items that are then consider social networking applications and how related to known items(e. g. books by the same author) they fit into the rs context this is followed by a section Familiar items can generate trust in the system but it presenting the qualitative and quantitative studies can also make the recommendations seem too exploring the integration of Rs and social networking The last section then discusses the implications of this inference leading to a recommendation(and agreeing research for rs design and future research with it)not only increases trust in the recommendation and the system providing it but also makes it more likely that the user will follow the recommendation 2. Background RSs have been deployed contexts, such as book or music shopping, e.g. Amazon conducted an extensive study examining what effect (www.amazon.com),generalratingsites(www.explanationsforcollaborativefilteringresultshaveon ratingzone. com), and specific rating sites such as the user's perception of the system. In testing different Movielens(http:/iMovielens.umnedu explanation interfaces, they found that explanations are important to users, because their own reasoning often RSs aggregate the information received and redirect does not match the inference mechanism of the system it to the appropriate recipients. While many strategies Users were less likely to trust recommendations when for computing recommendations have been explored they did not understand why certain items were such as item-based collaborative filtering [6, Bayesian recommended to them Herlocker et al [ 3] suggest that networks [7 and factor analysis [8, user-user a rating histogram of the user' s closest neighbours is collaborative filtering(CF)comes closest to emulating the most effective wa real-world recommendations because they are based on collaborative filtering y of explaining the results of the user rather than item matching. Recommendations are generated for a given user by comparing the These above studies took an evaluation approach to existing ratings to those of all other users in the improving an existing RS In our view, however, the user database. In doing so, a neighbourhood of similar users requirements for RSs have not been effectively is established, and based on that, rating predictions are investigated. Rather than continuing the current cycle computed for items that users have not yet rated, but of deploying a matching algorithm and seeing how users closest neighbours have respond, we took a step back and examined existing literature on advice-seeking and decision-making 2.1 Recommender systems research strategies, to identify what support users seek during RS research to date has focused on designing these activities algorithms for more effective and efficient computation of rating predictions The former aims to increase the 2.2 Trust research and recommender systems precision of predicting ratings. This is tested through Previous Hcl studies on rs aimed to increase user trust existing rating data sets, where part of the rating set is in these systems by helping users understand how the deleted and the prediction results from algorithms are system calculates recommendations. Related HCI compared against the real ratings. Prediction efficiency research in recent years has investigated trust in is concerned with the computational cost in terms of e Commerce systems [10, social networking virtual time and resources for calculating these predictions communities and recommender systems [11, 12]. Each BT Technology Journal.Vol 24 No 3. July 2006‘Knowing me, knowing you’ — using profiles and social networking to improve recommender systems BT Technology Journal • Vol 24 No 3 • July 2006 85 such domains and which recommender characteristics are important to them. Human-computer interaction (HCI) approaches to RS research have primarily focused on evaluating the user interface of existing RSs [3—5]. Our research, however, took a step back and asked when users seek recommendations, and what information they hope to receive. We translated our findings into a mock-up design of an RS which incorporates social networking features, and then evaluated those designs through experiments. This paper first presents a brief review of RS research to date, followed by an overview of the psychology literature on advice-seeking and decision making. We then consider social networking applications and how they fit into the RS context. This is followed by a section presenting the qualitative and quantitative studies exploring the integration of RS and social networking. The last section then discusses the implications of this research for RS design and future research. 2. Background RSs have been deployed in various eCommerce contexts, such as book or music shopping, e.g. Amazon (www.amazon.com), general rating sites (www. ratingzone.com), and specific rating sites such as MovieLens (http://movielens.umn.edu). RSs aggregate the information received and redirect it to the appropriate recipients. While many strategies for computing recommendations have been explored, such as item-based collaborative filtering [6], Bayesian networks [7] and factor analysis [8], user–user collaborative filtering (CF) comes closest to emulating real-world recommendations because they are based on the user rather than item matching. Recommendations are generated for a given user by comparing their existing ratings to those of all other users in the database. In doing so, a neighbourhood of similar users is established, and based on that, rating predictions are computed for items that users have not yet rated, but closest neighbours have. 2.1 Recommender systems research RS research to date has focused on designing algorithms for more effective and efficient computation of rating predictions. The former aims to increase the precision of predicting ratings. This is tested through existing rating data sets, where part of the rating set is deleted, and the prediction results from algorithms are compared against the real ratings. Prediction efficiency is concerned with the computational cost in terms of time and resources for calculating these predictions. HCI approaches to RS research have been limited to examining existing RSs in order to establish interaction design guidelines [3—5]. When comparing how advice from friends and from RS is perceived, Swearingen and Sinha [4, 9] found that, while people overall preferred recommendations from their friends, they appreciated the ability of an RS to provide serendipitous recommendations that broadened their horizons. In that context, Swearingen and Sinha [5, 9] identified two factors as fundamentally important in the overall usefulness of an RS — familiar recommendations and system transparency. Familiar recommendations can come in different forms — items previously consumed, or items that are related to known items (e.g. books by the same author). Familiar items can generate trust in the system, but it can also make the recommendations seem too simplistic. As for transparency, understanding the inference leading to a recommendation (and agreeing with it) not only increases trust in the recommendation, and the system providing it, but also makes it more likely that the user will follow the recommendation. Using a similar approach, Herlocker et al [3] conducted an extensive study examining what effect explanations for collaborative filtering results have on the user’s perception of the system. In testing different explanation interfaces, they found that explanations are important to users, because their own reasoning often does not match the inference mechanism of the system. Users were less likely to trust recommendations when they did not understand why certain items were recommended to them. Herlocker et al [3] suggest that a rating histogram of the user’s closest neighbours is the most effective way of explaining the results of collaborative filtering. These above studies took an evaluation approach to improving an existing RS. In our view, however, the user requirements for RSs have not been effectively investigated. Rather than continuing the current cycle of deploying a matching algorithm and seeing how users respond, we took a step back and examined existing literature on advice-seeking and decision-making strategies, to identify what support users seek during these activities. 2.2 Trust research and recommender systems Previous HCI studies on RS aimed to increase user trust in these systems by helping users understand how the system calculates recommendations. Related HCI research in recent years has investigated trust in eCommerce systems [10], social networking, virtual communities and recommender systems [11, 12]. Each