Recommender systems have emerged in various applications to support item recommendation[35, 38], solving the information-overload problem by suggesting items of interest to users. Various recommendation methods have been proposed. The collaborative filtering (CF) method [34] has been successfully used in various applications. It predicts user preferences for items in a word-of-mouth manner. User preferences are predicted by considering the opinions (in the form of preference ratings) of other “like-minded” users.
Recently, trust-based recommender systems [39] have incorporated the trustworthiness of users into the CF techniques to improve the quality of recommendation. According to [2], trust can be defined as how much a trustor believes that a trustee is willing and able to perform under a given situation. Massa et al. [24-27] proposed a trust recommender system based on a user’s web of trust, which explicitly specifies the friends s/he trusts. For instance, in Epinions.com, users are allowed to assign their personal trust value to the review writers. Through trust propagation from the web of trust, the trust value between two users can be predicted even though there is no direct trust value specified (connection) between them. Their work, however, relies on the user’s explicit assignment of trust value that is not easy to collect and may create a heavy burden on users.
Some researches [13, 15, 28] have proposed trust computation models to derive the trust value based on users’ past ratings of items. O’Donovan et al. [28] suggest that if a user has usually delivered accurate predictions in the past, s/he merits being called reliable and trustworthy. A prediction on an item contributed from a given user (producer) is accurate to a target user (consumer) if the difference between their ratings on the item is within a predefined error bound. Generally, a user is more trustworthy if s/he has contributed more accurate predictions than other users. Their proposed trust metrics is a global trust, which basically accumulates the given user’s accurate predictions made to other users or a group of users. Their trust model includes the item level and profile level. The
item-level / profile-level trust metric of a given user is derived by computing the ratio of accurate predictions that s/he has made to other users over a particular item / all items that s/he has rated in the past. In addition, Hwang and Chen [13] propose a relationship trust metric to derive the trust value between two users by calculating the ratio of accurate predictions over all co-rated items, i.e., those items that have been rated by both of them. The proposed relationship trust metric is more personalized than the reputation trust metric. Their proposed trust metrics are combined with the standard CF technique to improve prediction quality for a MovieLens dataset.
Nevertheless, no one has derived trust value based on a sequence of user’s ratings of items. In the MovieLens dataset, a user only has one rating score on an item and there is no ordering relationship between the items (movies) in a user’s rating history. That is, it does not matter whether a user saw a horror movie first and then a comedy movie, or a comedy movie first and then a horror movie. In knowledge-intensive environments, users normally have various information needs in accessing required documents over time, producing a sequence of documents ordered according to their access time. For such environments, the ordering of documents required by a user may be important. For example, a user may need to access documents with prerequisite and basic knowledge first and then documents with advanced knowledge.
In this work, we propose a sequence-based trust model to derive trust value based on users’
sequences of document ratings. The proposed model considers time factor, document similarity and user’s profile in computing the trustworthiness of users. Generally, an accurate prediction made in the recent past contributes more trustworthiness than one made earlier. Moreover, conventional trust computational models use the ratings on the same item to derive the accuracy of prediction and compute the trust value. In knowledge-intensive environments, users often have the information needs to access documents with similar contents. A user’s rating of a document generally reflects the user’s perception of the relevance of the document content to his/her information needs. Thus, the ratings on different documents with similar contents should also help to derive the trustworthiness of users.
Accordingly, we consider the time factor and the ratings on similar documents to derive a
sequence-based trust computation model. In addition, the recommended item is a text-based document, thus, content analysis is useful to select neighbors based on the similarity of user profiles which reveal users’ interest on document content. The proposed model is incorporated into the standard CF method to effectively discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of the CF method compared with other trust-based recommender systems.
The paper is organized as follows. We present the related works in Section 2. Section 3 describes our proposed trust computation models and the recommendation methods based on these models. The experiment results and evaluations are presented in Section 4. Finally, Section 5 describes the conclusions and future works.