Knowing me, knowing you'-using profiles and social networking to improve recommender systems 回回回网回 effort recommender decision onsiderations advice seeking Fig 1 Advice-seeking modeL. important when seeking advice in taste domains but know that this source is either very knowledgeable it might seem common sense that people would ce or is known to give good advice this can increase trust their friends for recommendations for cds or in a first-time encounter participants clearly pointed out that the relation to the recommender alone is not sufficient. In addition to 3.2 Study 2-profile similarity conditions has to be fulfilled before an advice-seeker will wanted to examine what effects different recommender trust a recommender: characteristics would have on people' s choices in an RS simulation. More specifically, what combination of either the advice seeker knows that the recom- familiarity profile similarity and rating overlap would mender has similar or the same tastes (taste have an influence on the choices people make in an rS context? Would a visualisation of profile similarity o or both the advice- seeker and recommender have between the decision maker and recomm sufficient mutual knowledge about each others, influence the decision maker's choice as suggested in tastes. so that even with taste differences . the social psychology and our previous study [ 14? Following recommender will be able to predict what the Perugini et als idea of modelling the user [3l and advice seeker will like representing their preferences and interests, we aimed to visualise a recommender in a way that would help the dge the appropriate 3.1.6 Decision process - trust and reliance recommendation We present an overview of the Past experience, source reputation and expertise have a experiment here while a more detailed account can be act on the final judgement of recommendation both from a known or unknown source. They tend to increase or decrease the level of 3.2.1 Method trust in, or reliance on, any given advice In this context, Since every participant would be different in terms of we define trust as faith in a known advisor in a first time demographic data, interests and tastes, we had to context, whereas reliance is based on past experience. create an experiment that would adapt to each Past experience simply means once advice seekers have individual participant, while conceptually remaining received good recommendations, they tend to stick with consistent for everyone To do this, we devised a film a particular recommender both the reputation and the festival scenario where participants receive fictitious expertise of a recommender can increase the trust in a movie recommendations from recommenders (gen first-time encounter. Equally, even if they have not erated on the fly) that were familiar or unfamiliar, similar received any advice from a particular advisor in the past or dissimilar, and either had the same or different film 90 BT Technology Journal. Vol 24 No 3. July 2006‘Knowing me, knowing you’ — using profiles and social networking to improve recommender systems 90 BT Technology Journal • Vol 24 No 3 • July 2006 important when seeking advice in taste domains. While it might seem common sense that people would consult their friends for recommendations for CDs or films, participants clearly pointed out that the relation to the recommender alone is not sufficient. In addition to knowing the recommender, one of two important conditions has to be fulfilled before an advice-seeker will trust a recommender: • either the advice seeker knows that the recom￾mender has similar or the same tastes (taste overlap), • or both the advice-seeker and recommender have sufficient mutual knowledge about each others’ tastes, so that even with taste differences, the recommender will be able to predict what the advice seeker will like. 3.1.6 Decision process — trust and reliance Past experience, source reputation and expertise have a significant impact on the final judgement of a recommendation, both from a known or unknown source. They tend to increase or decrease the level of trust in, or reliance on, any given advice. In this context, we define trust as faith in a known advisor in a first time context, whereas reliance is based on past experience. Past experience simply means once advice seekers have received good recommendations, they tend to stick with a particular recommender. Both the reputation and the expertise of a recommender can increase the trust in a first-time encounter. Equally, even if they have not received any advice from a particular advisor in the past, but know that this source is either very knowledgeable or is known to give good advice, this can increase trust in a first-time encounter. 3.2 Study 2 — profile similarity With the results from the qualitative study in mind, we wanted to examine what effects different recommender characteristics would have on people’s choices in an RS simulation. More specifically, what combination of familiarity, profile similarity and rating overlap would have an influence on the choices people make in an RS context? Would a visualisation of profile similarity between the decision maker and recommender influence the decision maker’s choice as suggested in social psychology and our previous study [14]? Following Perugini et al’s idea of modelling the user [31] and representing their preferences and interests, we aimed to visualise a recommender in a way that would help the decision maker judge the appropriateness of a recommendation. We present an overview of the experiment here while a more detailed account can be found in an earlier paper [32]. 3.2.1 Method Since every participant would be different in terms of demographic data, interests and tastes, we had to create an experiment that would adapt to each individual participant, while conceptually remaining consistent for everyone. To do this, we devised a film festival scenario where participants receive fictitious movie recommendations from recommenders (gen￾erated on the fly) that were familiar or unfamiliar, similar or dissimilar, and either had the same or different film Fig 1 Advice-seeking model. A1 A2 A3 A4 A5 A6 advice weighting A1 A2 A3 A4 A5 own influencing factors - own expertise - advisor expertise - advice confirms/ contradicts own opinion receiving advice 2 3 4 objective domain choice experience • cinema/theatre • restaurant consumption • books • CDs risk • financial • other people • consequences known - personally 1) taste overlap 2) mutual knowledge unknown - no personal contact - reviews - experts - people low cognitive effort trust/ reliance 1 decision maker ? 2 3 5 high cognitive effort past experience with source • source reputation • source expertise influencing factors choice taste domain advice seeking advice seeking item considerations recommender considerations decision process