Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

Collaborative filtering algorithms haven been widely used in recommender systems. However, they often suffer from the data sparsity and cold start problems. With the increasing popularity of social media, these problems may be solved by using social-based recommendation. Social-based recommendation, as an emerging research area, uses social information to help mitigate the data sparsity and cold start problems, and it has been demonstrated that the social-based recommendation algorithms can efficiently improve the recommendation performance. However, few of the existing algorithms have considered using multiple types of relations within one social network. In this paper, we investigate the social-based recommendation algorithms on heterogeneous social networks and proposed Hete-CF, a Social Collaborative Filtering algorithm using heterogeneous relations. Distinct from the exiting methods, Hete-CF can effectively utilize multiple types of relations in a heterogeneous social network. In addition, Hete-CF is a general approach and can be used in arbitrary social networks, including event based social networks, location based social networks, and any other types of heterogeneous information networks associated with social information. The experimental results on two real-world data sets, DBLP (a typical heterogeneous information network) and Meetup (a typical event based social network) show the effectiveness and efficiency of our algorithm

Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

The work described here was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 61373051; the National Science and Technology Pillar Program (Grant No.2013BAH07F05), the Key Laboratory for Symbolic Computation and Knowledge Engineering, Ministry of Education, China, and the UK Economic & Social Research Council (ESRC); award reference: ES/M001628/1.Preprin

Penerapan Metode Social-Based dan Item-Based Collaborative Filtering pada Mesin Pencarian

ABSTRAKSI: Collaborative Filtering dapat digunakan dalam melakukan pemilahan dokumen yang jumlahnya berlimpah. Terdapat dua pendekatan metode dalam ini. Pendekatan yang pertama adalah pemilahan dengan dasar kedekatan antar pengguna dalam memilih informasi dan yang kedua adalah kedekatan antara dokumen yang dipilih setiap pengguna dengan dokumen lainnya. Dengan menggabungkan dua pendekatan ini, diharapkan memiliki ketepatan serta kecepatan dalam melakukan prediksi informasi.Dalam merepresentasikan kedekatan sosial antar pengguna digunakan socialbased collaborative filtering yang melakukan penilaian kedekatan pengguna berdasarkan sejarah penilaian suatu dokumen oleh individu. Kemudian, dalam menghasilkan peringkat dokumen digunakan item-based collaborative filtering yang melakukan penilaian antar dokumen dengan memanfaatkan nilai hasil kedekatan sosial. Untuk setiap pencarian yang dilakukan dengan mesin pencarian, peringkat dokumen tersebut akan dijadikan referensi untuk kelayakan dokumen terhadap individu pencari. Hasil akhir dari proses ini adalah pemeringkatan dokumen berdasarkan pengguna tertentu.Kata Kunci : social-based collaborative filtering, item-based collaborativeABSTRACT: Collaborative Filtering is commonly used for document filtering with large amount of document. This method has two method approaches. The first approach was the user likeness between the user and second was the nearness between the document that chosen by each user and the other document. By uniting two approaches, it was hoped had the accuracy as well as the speed in carrying out the prediction of information.We used social-based collaborative filtering for representing the social nearness between the user. The assessment of the nearness of the user was based on the history of the assessment of a document by the individual. Afterwards, in producing the level of the document we used item-based collaborative filtering that carried out the assessment between the document by making use of the value of the social nearness of results. For each search that was carried out with the search engine, the level of this document will be made the reference for the appropriateness of the document against the seeker\u27s individual. Results of the end of this process retrieval is the The level of document was based on the certain user.Keyword: social-based collaborative filtering, item-based collaborative filtering

On social networks and collaborative recommendation

Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided annotations and implicit aggregated feedback describing the personal preferences of each user. It is also a common tendency for these systems to encourage the creation of virtual networks among their users by allowing them to establish bonds of friendship and thus provide a novel and direct medium for the exchange of data. We investigate the role of these additional relationships in developing a track recommendation system. Taking into account both the social annotation and friendships inherent in the social graph established among users, items and tags, we created a collaborative recommendation system that effectively adapts to the personal information needs of each user. We adopt the generic framework of Random Walk with Restarts in order to provide with a more natural and efficient way to represent social networks. In this work we collected a representative enough portion of the music social network last.fm, capturing explicitly expressed bonds of friendship of the user as well as social tags. We performed a series of comparison experiments between the Random Walk with Restarts model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.</p