Knowing me, knowing you'-using profiles and social networking to improve recommender systems of these aims to address different aspects of the source credibility concept of trust depending on the definition applied advice personalisation, Massa and Avesani [1l] addressed the problem of data sparseness in collaborative filtering -in a huge database, on average two users are unlikely to have rated the same items in a similar fashion To overcome Source credibility refers to the overall impression a site, personalisation to whether the information is this problem, they create a trust graph based on the tailored to the user 's needs, and predictability to degree of connectedness of users, using the distance whether the information presented reflects the users terms of arcs between two users on this graph as a knowledge, and prior experience with this and other measure of trust. This transitive trust graph is then used to increase the number of comparable users sites. However, Briggs et al actually examine infor mation seeking, rather than advice-seeking, and which O'Donovan and Smyth [12] take a different trustworthy elements of a site design make the information appear approach by using past rating reliability to generate trust values that increase or decrease the weight given to predictions of neighbouring users. They divide trust considering established decision-making and advice- into two possible categories- profile level and item level trust. On the profile level they compare all eeking research. For an rs to address users'needs in this domain, however, the requirements of the users common existing ratings of an advice- seeker and a task need to be considered, recommender. and examine whether the recommender would have been able to predict the correct ratings for a given advice-seeker The percentage of correct pre- The next section gives a brief overview of the dictions across all common items is their profile- level evant psychology literature on this issue, and trust value. Item-level trust is more fine-grained and identifies the support requirements which can be only measures the percentages of recommendations for inferred from it. A more detailed description can be one item that were correct found in an earlier paper [14 Each of these approaches addresses a different 2.3 Decision making and advice seeking hallenge for collaborative filtering (CF)algorithms. The main purpose of an RS is to aid its users in their Whereas the Massa and Avesani [11] approach can be decision making by presenting a reduced set of options used to overcome a technical problem in order to in the form of advice or recommendations. To [12] mainly aims to increase accuracy(precision)of such other sources it is instructive to examine the decision a CF algorithm. However, both approaches address a making and advice-seeking literature in psychology partici echnical problem without considerin user's perspective on trust. 2.3.1 Decision making Riegelsberger et al [10] developed a trust model for Classic decision-making theory proposes interactions between two actors in a situation of strategies that decision makers might adopt information or advice. The model divides means of under the compensatory models of decision making signalling trustworthiness into symbols and symptoms, [16] would predict that people make decisions based both of which the technology involved can transmit on the expected utility of an option. Evidence, however Symbols can be e Commerce trust seals or a professional Suggests that decision makers often use various, less precise non-compensatory models. Decision makers be specifically created; rather they are a by-product of often do not make decisions in the rational or logica way proposed by utility theory but rather they are often recommender who repeatedly gives good advice to an influenced by a plethora of non-rational factors of tr who has given good advice to others may become a 2.3.2 Advice seeking symbol of trustworthiness The advice-seeking literature has predominantly focused on objective domains where advice can be Briggs et al [13 identified trust-warranting factors classified as right or wrong [17-20 Generally, advice for on-line advice. They concluded that three factors seeking is seen as a problem of combining and were paramount for users to perceive information as weighting different information sources to come to a trustworthy final conclusion 86 BT Technology Journal·vol24No3·July2006‘Knowing me, knowing you’ — using profiles and social networking to improve recommender systems 86 BT Technology Journal • Vol 24 No 3 • July 2006 of these aims to address different aspects of the concept of trust depending on the definition applied. Massa and Avesani [11] addressed the problem of data sparseness in collaborative filtering — in a huge database, on average two users are unlikely to have rated the same items in a similar fashion. To overcome this problem, they create a trust graph based on the degree of connectedness of users, using the distance in terms of arcs between two users on this graph as a measure of trust. This transitive trust graph is then used to increase the number of comparable users. O’Donovan and Smyth [12] take a different approach by using past rating reliability to generate trust values that increase or decrease the weight given to predictions of neighbouring users. They divide trust into two possible categories — profile level and item level trust. On the profile level they compare all common existing ratings of an advice-seeker and a recommender, and examine whether the recommender would have been able to predict the correct ratings for a given advice-seeker. The percentage of correct pre￾dictions across all common items is their profile-level trust value. Item-level trust is more fine-grained and only measures the percentages of recommendations for one item that were correct. Each of these approaches addresses a different challenge for collaborative filtering (CF) algorithms. Whereas the Massa and Avesani [11] approach can be used to overcome a technical problem in order to generate recommendations more quickly, O’Donovan’s [12] mainly aims to increase accuracy (precision) of such a CF algorithm. However, both approaches address a particular technical problem without considering the user’s perspective on trust. Riegelsberger et al [10] developed a trust model for interactions between two actors in a situation of uncertainty and risk, such as an exchange of goods, information or advice. The model divides means of signalling trustworthiness into symbols and symptoms, both of which the technology involved can transmit. Symbols can be eCommerce trust seals or a professional looking Web site. Symptoms, on the other hand, cannot be specifically created; rather they are a by-product of trustworthy actions. In the context of an RS, a recommender who repeatedly gives good advice to an individual shows evidence of trustworthiness. Someone who has given good advice to others may become a symbol of trustworthiness. Briggs et al [13] identified trust-warranting factors for on-line advice. They concluded that three factors were paramount for users to perceive information as trustworthy: • source credibility, • advice personalisation, • predictability. Source credibility refers to the overall impression of a site, personalisation to whether the information is tailored to the user’s needs, and predictability to whether the information presented reflects the user’s knowledge, and prior experience with this and other sites. However, Briggs et al actually examine infor￾mation seeking, rather than advice-seeking, and which elements of a site design make the information appear trustworthy. In RS research, there has been a significant lack of considering established decision-making and advice￾seeking research. For an RS to address users’ needs in this domain, however, the requirements of the user’s task need to be considered. The next section gives a brief overview of the relevant psychology literature on this issue, and identifies the support requirements which can be inferred from it. A more detailed description can be found in an earlier paper [14]. 2.3 Decision making and advice seeking The main purpose of an RS is to aid its users in their decision making by presenting a reduced set of options in the form of advice or recommendations. To understand how people normally deal with advice from other sources, it is instructive to examine the decision￾making and advice-seeking literature in psychology. 2.3.1 Decision making Classic decision-making theory proposes various strategies that decision makers might adopt when examining options [15, 16]. Utility theory, which falls under the compensatory models of decision making [16], would predict that people make decisions based on the expected utility of an option. Evidence, however, suggests that decision makers often use various, less precise non-compensatory models. Decision makers often do not make decisions in the rational or logical way proposed by utility theory, but rather they are often influenced by a plethora of non-rational factors. 2.3.2 Advice seeking The advice-seeking literature has predominantly focused on objective domains where advice can be classified as right or wrong [17—20]. Generally, advice seeking is seen as a problem of combining and weighting different information sources to come to a final conclusion