Mobile Multimedia Recommendation in Smart Communities: A Survey <s> 1) Mobile Device Limitations and Connectivity <s> Traditionally, collaborative filtering (CF) algorithms used for recommendation operate on complete knowledge. This makes these algorithms hard to employ in a decentralized context where not all users' ratings can be available at all locations. In this paper we investigate how the well-known neighbourhood-based CF algorithm by Herlocker et al. operates on partial knowledge; that is, how many similar users does the algorithm actually need to produce good recommendations for a given user, and how similar must those users be. We show for the popular MovieLens 1,000,000 and Jester datasets that sufficiently good recommendations can be made based on the ratings of a neighbourhood consisting of a relatively small number of randomly selected users. <s> BIB001 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> 1) Mobile Device Limitations and Connectivity <s> A self adaptive communication protocol is proposed for peer to peer distributed computing. This protocol can configure itself automatically in function of application requirements and topology changes by choosing the most appropriate communication mode between peers. The protocol was designed in order to be used in conjunction with a decentralized environment for high performance distributed computing. A first set of computational experiments is also presented and analyzed for an optimization application, i.e. nonlinear network flow problems. <s> BIB002 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> 1) Mobile Device Limitations and Connectivity <s> Peer-to-Peer multikeyword searching requires distributed intersection/union operations across wide area networks, raising a large amount of traffic cost. Existing schemes commonly utilize Bloom Filters (BFs) encoding to effectively reduce the traffic cost during the intersection/union operations. In this paper, we address the problem of optimizing the settings of a BF. We show, through mathematical proof, that the optimal setting of BF in terms of traffic cost is determined by the statistical information of the involved inverted lists, not the minimized false positive rate as claimed by previous studies. Through numerical analysis, we demonstrate how to obtain optimal settings. To better evaluate the performance of this design, we conduct comprehensive simulations on TREC WT10G test collection and query logs of a major commercial web search engine. Results show that our design significantly reduces the search traffic and latency of the existing approaches. <s> BIB003
Mobile device challenges and limitations such as limited display/screen sizes, low battery lives, limited processing power, limited storage and limited input capabilities affect their usability practices. Recent advances in microprocessors architectures and high bandwidth networks permit one to consider high performance Peer-to-Peer (P2P) distributed computing as an economic and attractive solution to mobile multimedia recommender systems BIB002 - BIB001 . For example, Chen et al. BIB003 designed and optimized Bloom Filter settings in a peer-to-peer mutli-keyword searching which requires intersection operations across Wide Area Networks (WANs). Another technical challenge in this regard is sustainable network connectivity. The mobility of such recommender systems requires efficient, effective and reliable network technologies for sustainability.