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The tremendous growth of information on the Internet has been above our ability to process. A recommender system, which filters out useful information and generate recommendations, has been introduced to help users overcome the information overload problem and has been widely applied in an ever-increasing number of e-commercial websites. Collaborative filtering and content-based recommendation methods are two major approaches used in recommender systems. The collaborative filtering predicts items which a particular user prefers by using a database about the past preferences of users with similar interests. The content-based method analyses the content of the objects to generate a representative list of the user’s interests, and then compares the similarity of item descriptions. These two methods have some drawbacks in dealing with situations such as sparse data and cold start problems. Recently, hybrid methods combining collaborative filtering and content-based methods have been proposed to overcome these limitations. However, personalized recommender system attempt to penetrate people’s various demand and generate the tailored recommendations. A highly effective and personalised recommender system may still face new challenges including interestdrifting and multicriteria optimisation. For example, a user’s interest may change over time. They may no longer like a item which was strongly preferred. Another example is that a person’s preference is varying and always has multiple criteria. Classic collaborative filtering uses a single overall rating for prediction. It does not properly reflect the opinion on a item and the reason why people rated this item high or low. Unfortunately, the current recommender systems do not consider these important factors. First, we proposed a novel hybrid recommender system to overcome interest-drifting by embedding the time-sensitive functions into the recommendation process. The experimental results show that the intergraded approach with interest-drifting can constantly perform better and provide users with higher quality recommendations. Meanwhile, the experimental results on different size of training dataset show that our algorithm can boost the prediction accuracy for all configurations. The contributions of this proposed algorithm are in two main aspects. First, using time function to reflect users’ intersts changing in order to achieve higher quality of recommendations. Second, using intergraded methods to solve some problems such as sparsity and cold start. Then we developed a new technique to aggregate the multicriteria ratings for predicting more accurate recommendations. The results show that our algorithms outperforms the traditional collaborative filtering recommender system on both accuracy of predicting ratings and accuracy of recommendations. The one of contributions in this proposed method is that we introduced the multicriteria concept into recommender systems to reflect the users’ opinion more accurate. Another contribution is that we develop a linear method to aggregate multicriteria to single rating for higher quality of recommendations. Our experiments demonstrate that the recommendation achieved better performances when interest-drifting and multicriteria ratings were considered. The significance of our research study is that we consider incorporating interest-drifting, and multicriteria ratings into a recommender system to generate personalised and effective recommendations.

Whenever autonomous entities work together to meet each other's needs, there arises the problem of how an entity with a need can find and use entities with the capability to meet that need. This problem is seen in Web service architectures, agent systems, and data integration systems, among others. Solutions have been proposed in each of these fields, but they are all dependent on implementation and interface. Hence all are restricted to their particular field, and all require their participants to conform to certain assumptions about implementation and interface. This failure of support for service autonomy is conceptually unattractive and impractical. In this thesis we show how to describe and matchmake service capabilities and client needs in a way that is implementation and interface independent. The result is a service discovery solution that fully supports the rights of services to choose their own implementation and interface. Our representation is capable of capturing capabilities across a range of service types, from Web services to agents to data sources, while ignoring the implementation and interface details that distinguish them. Thus, our solution unifies these fields for description and discovery purposes, allowing data sources with complex language interfaces to compete against form-based Web services and frame-and-slot agents, for example. Moreover, our solution captures all of the most important aspects of capability, such as: the conceptual meaning and limitations on what a service can achieve; what requests can be expressed through a service's interface, and limitations on what attributes of information a service can return. The provision of an interface independent capability description raises the additional question of how to enable a client to invoke the service to which it has been matched, and correctly interpret the results returned; we solve this by providing an interface description that maps from client objectives onto invocations, and from returned results onto a canonical result format.