Semantic Web Interaction through Trust Network Recommender Systems

In this paper, we present FilmTrust, a website that integrates Semantic Web-based social networks, augmented with trust, to create predictive movie recommendations. We show how these recommendations are more accurate than other techniques in certain cases, and discuss this technique as a mechanism of Semantic Web interaction. Trust in social networks on the Semantic Web is a topic that has gained increased interest in the last few years. Using FOAF as the basis for the social network, trust has been encoded using the FOAF Trust Module or computed from other information. With these trust values as a starting place, several algorithms for inferring trust relationships have been introduced. This analysis of Semantic Web-based social networks has produced results, but their usefulness in the space of user interaction has not been fully addressed. In this paper, we present FilmTrust, a website that integrates Semantic Web-based social networking into a movie recommender system. We begin with a description of the FilmTrust website, followed by an analysis of its features. TidalTrust, a trust network inference algorithm, is used as the basis for generating predictive ratings personalized for each user. The accuracy of the recommended ratings is shown to outperform both a simple average rating and the ratings produced by a common recommender system algorithm. Theoretically and through a small user study, some evidence is also established that supports a user benefit from ordering reviews based on the users' trust preferences. 1 Background and Related Work Social Network data, represented using the FOAF Vocabulary[1], is some of the most prevalent data on the Semantic Web. TidalTrust[2] is an algorithm for inferring trust relationships. Using a recursive search with weighted averages, it can take two people in the network and generate a recommendation about how much one person should trust the other, based on the paths that connect them in the network, and the trust ratings on those paths. Part of our recommender system relies on inferred trust ratings, and this is the algorithm that is used there. 1 http://trust.mindswap.org/ont/trust.owl Recommender systems help users identify items of interest. These recommendations are generally made in two ways: by calculating the similarity between items and recommending items related to those in which the user has expressed interest, or by calculating the similarity between users in the system and recommending items that are liked by similar users. This latter method is also known as collaborative filtering. Collaborative filtering has been applied in many contexts, and FilmTrust is not the first to attempt to make predictive recommendations about movies. MovieLens [5], Recommendz [6], and Film-Conseil [7] are just a few of the websites that implement recommender systems in the context of films. Herlocker, et al. [8] present an excellent overview of the goals, datasets, and algorithms of collaborative filtering systems. However, FilmTrust is unlike the approach taken in many collaborative filtering recommender systems in that its goal is not to present a list of good items to users; rather, the recommendations are generated to suggest how much a given user may be interested in an item that the user already found. For this to work, there must be a measure of how closely the item is related to the user's preferences. In this work, the aim is to use trust ratings within the social network as the basis for making calculations about similarity. For this technique to be successful, there must be a correlation between trust and user similarity. AbdulRahman and Hailes [9] showed that in a predefined context, such as movies, users develop social connections with people who have similar preferences. These results were extended in work by Ziegler and Lausen [10]. Their work showed a correlation between trust and user similarity in an empirical study of a real online community. Other work has touched on trust in recommender systems, including [11] and [12]. These works address the use of trust within systems where the set of commonly rated items between users is sparse. That situation leads to a breakdown in correlation-based recommender system algorithms, and their work explores how incorporating even simple binary trust relationships can increase the coverage and thus the number of recommendations that can be made. 2 The FilmTrust Website The social networking component of the website requires users to provide a trust rating for each person they add as a friend. When creating a trust rating on the site, users are advised to rate how much they trust their friend about movies. In the help section, when they ask for more help, they are advised to, "Think of this as if the person were to have rented a movie to watch, how likely it is that you would want to see that film." Part of the user's profile is a "Friends" page,. In the FilmTrust network, relationships can be one-way, so users can see who they have listed as friends, and vice versa . If trust ratings are visible to everyone, users can be discouraged from giving accurate ratings for fear of offending or upsetting people by giving them low ratings. Because honest trust ratings are important to the function of the system, these values are kept private and shown only to the user who assigned them. The other features of the website are movie ratings and reviews. Users can choose any film and rate it on a scale of a half star to four stars. They can also write free-text reviews about movies. Social networks meet movie information on the "Ratings and Reviews" page shown in Figure 2. Users are shown two ratings for each movie. The first is the simple average of all ratings given to the film. The "Recommended Rating" uses the inferred trust values, computed with TidalTrust on the social network, for the users who rated the film as weights to calculate a weighted average rating. Because the inferred trust values reflect how much the user should trust the opinions of the person rating the movie, the weighted average of movie ratings should reflect the user's opinion. If the user has an opinion that is different from the average, the rating calculated from trusted friends – who should have similar opinions – should reflect that difference. Similarly, if a movie has multiple reviews, they are sorted according to the inferred trust rating of the author. This presents the reviews authored by the most trusted people first to assist the user in finding information that will be most relevant. 3 Site Personalization 3.1 Computing Recommended Movie Ratings One of the features of the FilmTrust site that uses the social network is the "Recommended Rating" feature. As figure 7.3 shows, users will see this in addition to the average rating given to a particular movie. The "Recommended Rating" is personalized using the trust values for the people who have rated the film (the raters). The process for calculating this rating is very similar to the process for calculating trust ratings presented in chapter 6. First, the system searches for raters that the source knows directly. If there are no direct connections from the user to any raters, the system moves one step out to find connections from the user to raters of path length 2. This process repeats until a path is found. The opinion of all raters at that depth are considered. Then, using TidalTrust, the trust value is calculated for each rater at the given depth. Once every rater has been given an inferred trust value, only the ones with the highest ratings will be selected; this is done by simply finding the maximum trust value calculated for each of the raters at the selected depth, and choosing all of the raters for which that maximum value was calculated. Finally, once the raters have been selected, their ratings for the movie (in number of stars) are averaged. For the set of selected nodes S, the recommended rating r from node s to movie m is the average of the movie ratings from nodes in S weighted by the trust value t from s to each node: