Knowing me, knowing you'-using profiles and social networking e recommender systems definition of quality) in reducing some of the inherent or CDs to buy, which films to see, in which restaurant to weaknesses: these include the decision maker's failure eat or even how to find a reliable dentist. to generate sufficient alternatives for choice and the tendency to try to confirm rather than challenge prior 3. 1.1 Method views. Yaniv suggests that the best way to study this Twelve semi-structured interviews and five fo domain is to examine the role of a personal match were conducted with a total number of 44 pa between the advice giver and seeker this assumes that with an age range from 21 to 46, including the greater the similarity between them the greater the students as well as professionals. the aim was to elicit benefit from receiving advice concepts and priorities that are important to decision makers when seeking advice, which would then guide This is exactly what collaborative filtering rss do he subsequent focus groups matching users according to their tastes as expressed through item ratings, and basing their recom- 3.1.2 Results mendations on that similarity correlation To analyse the data and model the advice-seeking strategies and processes, we drew on an established widespread forms of getting to know each other is approach from social psychology called grounded theory establishing some form of common ground in our likes 30]. We present the key findings here, summarised in and dislikes. This is a trust-building exercise as it helps Fig l, while readers can refer to our previous paper for a evaluate our counterpart's personality. Indeed, social more detailed account [14 psychology has shown that people like others who are, 3.1.3 Domains of interest among other things, familiar, similar to themselves and with whom they have a history of interaction [(291 Participants clearly differentiated between objective and taste domains. Objective item domains items are Friends from whom we seek recommendations are characterised by measurable and comparable not just a source of information for us - we know their specifications. These are perceived as being neutral to tastes,views and they provide not only different ideas of taste. Items that fall into this category recommendations, but also justification are things like electronic goods, computer hardware and explanations for them. If in doubt we can always software, and cars. Items in taste domains are seen question their recommendations by simply asking about far harder to find advice on. One item may be rated their reasoning and referring back to previous differently by two recommenders, yet both can justi recommendations we might have received from them their ratings. Examples for such items are music, books films and restaurants Seeking and receiving a recommendation is a social activity that often involves the discussion of a particular 3.1.4 Item considerations -risk item. Why did the recommender like it? Would the Regardless of the item domain, a key consideration is recommender want to experiencelbuy it again? Will the the potential risk and the consequences in making a experience change after a while? hoice. At its most basic and common level one of the first considerations is a financial one('lt also comes Our main goal was therefore to explore in more down to the value of the thing ) Participants generally detail the factors that drive people's decision-making said the greater the financial risk involved in a particular improve rS design 9 and how this can be applied to choice, the more detailed and careful their research and advice. seek would be before coming to a decision Risk is associated with the following factors Empirical studies The studies presented in this paper aimed to address bought goods(e. g. books/CDs), that can easily be the above questions from different per rspectives returned bear a lower risk than experience first study was to lay the basis in establishing how consumed goods (e. g. cinema/restaurant visits), people actually seek advice in taste domains. The results would then inform the design of the second when another party is involved the risk is higher (e. g. romantic dinner date) study, which aimed to test the findings from the first study in an rs context. for services (e.g. lawyers, plumbers, dentists), people look for reassurance in a recommendation 3.1 Study 1 -looking for a good restaurant: 3.1.5 Recommender considerations known who to ask versus unknown Since rss aim to give advice in taste domains, we Across all interviews and focus groups, the relationship decided to investigate how people choose what books with the recommender was seen to be extremely 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 89 definition of quality) in reducing some of the inherent weaknesses; these include the decision maker’s failure to generate sufficient alternatives for choice and the tendency to try to confirm rather than challenge prior views. Yaniv suggests that the best way to study this domain is to examine the role of a personal match between the advice giver and seeker. This assumes that the greater the similarity between them, the greater the benefit from receiving advice. This is exactly what collaborative filtering RSs do — matching users according to their tastes as expressed through item ratings, and basing their recommendations on that similarity correlation. When meeting new people, one the most widespread forms of getting to know each other is establishing some form of common ground in our likes and dislikes. This is a trust-building exercise as it helps evaluate our counterpart’s personality. Indeed, social psychology has shown that people like others who are, among other things, familiar, similar to themselves and with whom they have a history of interaction [29]. Friends from whom we seek recommendations are not just a source of information for us — we know their tastes, views and they provide not only recommendations, but also justification and explanations for them. If in doubt we can always question their recommendations by simply asking about their reasoning and referring back to previous recommendations we might have received from them. Seeking and receiving a recommendation is a social activity that often involves the discussion of a particular item. Why did the recommender like it? Would the recommender want to experience/buy it again? Will the experience change after a while? Our main goal was therefore to explore in more detail the factors that drive people’s decision-making and advice-seeking and how this can be applied to improve RS design. 3. Empirical studies The studies presented in this paper aimed to address the above questions from different perspectives. The first study was to lay the basis in establishing how people actually seek advice in taste domains. The results would then inform the design of the second study, which aimed to test the findings from the first study in an RS context. 3.1 Study 1 — looking for a good restaurant: who to ask Since RSs aim to give advice in taste domains, we decided to investigate how people choose what books or CDs to buy, which films to see, in which restaurant to eat or even how to find a reliable dentist. 3.1.1 Method Twelve semi-structured interviews and five focus groups were conducted with a total number of 44 participants with an age range from 21 to 46, including university students as well as professionals. The aim was to elicit concepts and priorities that are important to decision makers when seeking advice, which would then guide the subsequent focus groups. 3.1.2 Results To analyse the data and model the advice-seeking strategies and processes, we drew on an established approach from social psychology called grounded theory [30]. We present the key findings here, summarised in Fig 1, while readers can refer to our previous paper for a more detailed account [14]. 3.1.3 Domains of interest Participants clearly differentiated between objective and taste domains. Objective item domains items are characterised by measurable and comparable specifications. These are perceived as being neutral to different ideas of taste. Items that fall into this category are things like electronic goods, computer hardware and software, and cars. Items in taste domains are seen as far harder to find advice on. One item may be rated differently by two recommenders, yet both can justify their ratings. Examples for such items are music, books, films and restaurants. 3.1.4 Item considerations — risk Regardless of the item domain, a key consideration is the potential risk and the consequences in making a choice. At its most basic and common level, one of the first considerations is a financial one (‘It also comes down to the value of the thing’). Participants generally said the greater the financial risk involved in a particular choice, the more detailed and careful their research would be before coming to a decision. Risk is associated with the following factors: • bought goods (e.g. books/CDs), that can easily be returned, bear a lower risk than experienced/ consumed goods (e.g. cinema/restaurant visits), • when another party is involved the risk is higher (e.g. romantic dinner date), • for services (e.g. lawyers, plumbers, dentists), people look for reassurance in a recommendation. 3.1.5 Recommender considerations — known versus unknown Across all interviews and focus groups, the relationship with the recommender was seen to be extremely
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 recommender 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 (generated 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