The integration of Linked Open Data (LOD) content in Web pages is a challenging and sometimes tedious task for Web developers. At the same moment, most software packages for blogs, content manage- ment systems (CMS), and shop applications support the consumption of feed formats, namely RSS and Atom. In this technical report, we demon- strate an on-line tool that fetches e-commerce data from a SPARQL endpoint and syndicates obtained results as RSS or Atom feeds. Our ap- proach combines (1) the popularity and broad tooling support of existing feed formats, (2) the precision of queries against structured data built upon common Web vocabularies like schema.org, GoodRelations, FOAF, VCard, and WGS 84, and (3) the ease of integrating content from a large number of Web sites and other data sources in RDF in general.
We compared our work with existing approaches, namely (1) single feed definition dialogs, as offered by major sites (e.g. eBay and Amazon), and (2) feed aggregation services, i.e. Yahoo Pipes http: pipes.yahoo.com and DERI Pipes @cite_6 . The former approaches typically fail at integrating different data sources (e.g. ), whereas aggregation services are limited to filter results by brittle regex-based expressions (e.g. ) and lack simple unit conversion (e.g. ) (cf. @cite_0 ).
@cite_0 @cite_6
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Over the last decade a vast number of businesses have developed online e-shops in the web. These online stores are supported by sophisticated systems that manage the products and record the activity of customers. There exist many research works that strive to answer the question "what items are the customers going to like" given their historic profiles. However, most of these works miss to take into account the time dimension and cannot respond efficiently when data are huge. In this paper, we study the problem of recommendations in the context of multi-relational stream mining. Our algorithm first separates customers based on their historic data into clusters. It then employs collaborative filtering (CF) to recommend new items to the customers based on their clustering structure. We evaluate our algorithm on two data sets, MovieLens and a synthetic data set. Collaborative Filtering (CF) is the most successful approach to Recommender Systems (RS). In this paper, we suggest methods for global and personalized visualization of CF data. Users and items are first embedded into a high-dimensional latent feature space according to a predictor function particularly designated to conform with visualization requirements. The data is then projected into 2-dimensional space by Principal Component Analysis (PCA) and Curvilinear Component Analysis (CCA). Each projection technique targets a di fferent application, and has its own advantages. PCA places all items on a Global Item Map (GIM) such that the correlation between their latent features is revealed optimally. CCA draws personalized Item Maps (PIMs) representing a small subset of items to a specifi c user. Unlike in GIM, a user is present in PIM and items are placed closer or further to her based on their predicted ratings. The intra-item semantic correlations are inherited from the high-dimensional space as much as possible. The algorithms are tested on three versions of the MovieLens dataset and the Netflix dataset to show they combine good accuracy with satisfactory visual properties. We rely on a few examples to argue our methods can reveal links which are hard to be extracted, even if explicit item features are available. Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based filtering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-crafted feature engineering, which is usually noise-prone and biased by different feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly models content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large-scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation. The behavior of users in certain services could be a clue that can be used to infer their preferences and may be used to make recommendations for other services they have never used. However, the cross-domain relationships between items and user consumption patterns are not simple, especially when there are few or no common users and items across domains. To address this problem, we propose a content-based cross-domain recommendation method for cold-start users that does not require user- and item- overlap. We formulate recommendation as extreme multi-class classification where labels (items) corresponding to the users are predicted. With this formulation, the problem is reduced to a domain adaptation setting, in which a classifier trained in the source domain is adapted to the target domain. For this, we construct a neural network that combines an architecture for domain adaptation, Domain Separation Network, with a denoising autoencoder for item representation. We assess the performance of our approach in experiments on a pair of data sets collected from movie and news services of Yahoo! JAPAN and show that our approach outperforms several baseline methods including a cross-domain collaborative filtering method.