Knowing me, knowing you'-using profiles and social networking e recommender systems tastes(in terms of rating overlap). the individua would see one of four 'buddy profiles and be told characteristics of the recommenders were adapted on whether these buddies had rated those films the the fly to each participant same way or not · Profile similarity 3.2.2 Participants and procedure A total of 100 participants completed the study whicl Profile similarity was based on the demographic lasted 5 minutes, in a computer laboratory. The data, film genre preferences and interests and had varied from 18-44, with a variety of two levels . similar or dissimilar. Thus a simil backgrounds, including students and professionals. ( two years), same profession and have Each participant encountered four phases(see Fig 2 significant overlap in their film genre preferences for an overview). In the first phase each pant was hobbies and interests. Profile similarity was required to provide a basic profile, consisting of visualised through highlighting of the interests demographic data such as age, gender and profession preferences the participant and recommender had but also preferences and interests such as preferred film genres, hobbies, leisure activities and music tastes. In phase 2, participants rated 20-30 films, which would a dissimilar profile on the other hand, would show little or no overlap in terms of interests and serve as a basis for generating their recommendations preferences and significantly differ in terms of demographic data. In phase 3, participants chose from a series of 48 pairs of films recommended by people who -the Rating Overlap scenario made out- had already seen those films Rating overlap was based on the film ratings Phase 4 consisted of a post-study questionnaire participants had previously supplied (phase 2) where participants rated some of the profiles they had Thus recommenders could either have high or low previously seen in terms of familiarity, profile similarity rating overlap, which was visualised in one of two rating overlap and trust, and provided qualitative data vays (consistent for each participant (in text fields)about their decision-reasoning text visualisation (similar to the profile similarity visualisation) showed by explicit highlighting which 3.2.3 Independent variables- recommender films the participant and the recommender had profile characteristics rated in a similar fashion The recommender profile characteristic variables are symbol visualisation showed a Venn diagram listed below with two overlapping circles, a large middle circle representing a large rating overlap and a small middle circle representing a small rating overlap Considering that people consult known sources for Film recommender profiles recommendations, we aimed to simulate familiarity with a recommender through repeated exposure as With these three independent variables, each with suggested in social psychology [29]and the advice. two levels (e. g. familiar versus unfamiliar), this seeking and decision-making literature 33 experiment was a2×2 Exposing participants to a limited number of combinations of these resulted in eight recom- profiles before actually receiving recommendations mender profiles as shown in Fig 3 simulates the process of getting to know that particular profile in relation to one's own. This 3.2.4 Dependent variable-choice variable had two levels, familiar and unfamiliar. In phase 3(see Fig 2 for details), participants saw the Thus during the rating phase of the experiment titles of completely fictitious films and the profile (phase 2, see Fig 2), after each rating participants information of the recommender. The reason for not register rate films choose films create profile with familiarisation with 4 48 pairs of films demographic data 8 different types of erification hobbies/interests recommender 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 91 tastes (in terms of rating overlap). The individual characteristics of the recommenders were adapted on the fly to each participant. 3.2.2 Participants and procedure A total of 100 participants completed the study, which lasted 30—45 minutes, in a computer laboratory. The age range varied from 18—44, with a variety of backgrounds, including students and professionals. Each participant encountered four phases (see Fig 2 for an overview). In the first phase, each participant was required to provide a basic profile, consisting of demographic data such as age, gender and profession, but also preferences and interests such as preferred film genres, hobbies, leisure activities and music tastes. In phase 2, participants rated 20—30 films, which would serve as a basis for generating their recommendations. In phase 3, participants chose from a series of 48 pairs of films recommended by people who — the scenario made out — had already seen those films. Phase 4 consisted of a post-study questionnaire, where participants rated some of the profiles they had previously seen in terms of familiarity, profile similarity, rating overlap and trust, and provided qualitative data (in text fields) about their decision-reasoning. 3.2.3 Independent variables — recommender profile characteristics The recommender profile characteristic variables are listed below. • Familiarity Considering that people consult known sources for recommendations, we aimed to simulate familiarity with a recommender through repeated exposure as suggested in social psychology [29] and the advice￾seeking and decision-making literature [33]. Exposing participants to a limited number of profiles before actually receiving recommendations simulates the process of getting to know that particular profile in relation to one’s own. This variable had two levels, familiar and unfamiliar. Thus during the rating phase of the experiment (phase 2, see Fig 2), after each rating, participants would see one of four ‘buddy’ profiles and be told whether these buddies had rated those films the same way or not. • Profile similarity Profile similarity was based on the demographic data, film genre preferences and interests and had two levels, similar or dissimilar. Thus a similar profile would be the same gender, similar age (± two years), same profession and have a significant overlap in their film genre preferences, hobbies and interests. Profile similarity was visualised through highlighting of the interests or preferences the participant and recommender had in common. A dissimilar profile, on the other hand, would show little or no overlap in terms of interests and preferences and significantly differ in terms of demographic data. • Rating Overlap Rating overlap was based on the film ratings participants had previously supplied (phase 2). Thus recommenders could either have high or low rating overlap, which was visualised in one of two ways (consistent for each participant): — text visualisation (similar to the profile similarity visualisation) showed by explicit highlighting which films the participant and the recommender had rated in a similar fashion, — symbol visualisation showed a Venn diagram with two overlapping circles, a large middle circle representing a large rating overlap and a small middle circle representing a small rating overlap. • Film recommender profiles With these three independent variables, each with two levels (e.g. familiar versus unfamiliar), this experiment was a 2 × 2 × 2 design. All possible combinations of these resulted in eight recom￾mender profiles as shown in Fig 3. 3.2.4 Dependent variable — choice In phase 3 (see Fig 2 for details), participants saw the titles of completely fictitious films and the profile information of the recommender. The reason for not Fig 2 Experiment overview. register – create profile with demographic data hobbies/interests rate films – familiarisation with 4 specific profiles choose films – 48 pairs of films – 8 different types of recommender post-study questionnaire – variable verification – profile judging instructions instructions instructions 1 2 3 4