A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit <a rel="external noopener" href="http://users.jyu.fi/~swang/publications/CIKM12-LR4RS.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<i title="ACM Press">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6g37zvjwwrhv3dizi6ffue642m" style="color: black;">Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12</a>
Most existing recommender systems can be classified into two categories: collaborative filtering and content-based filtering. Hybrid recommender systems combine the advantages of the two for improved recommendation performance. Traditional recommender systems are rating-based. However, predicting ratings is an intermediate step towards their ultimate goal of generating rankings or recommendation lists. Learning to rank is an established means of predicting rankings and has recently demonstrated<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2396761.2398610">doi:10.1145/2396761.2398610</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cikm/SunWGM12.html">dblp:conf/cikm/SunWGM12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/je6gcoyunba53arzkm3ks4ydki">fatcat:je6gcoyunba53arzkm3ks4ydki</a> </span>
more »... high promise in improving quality of recommendations. In this paper, we propose LRHR, the first attempt that adapts learning to rank to hybrid recommender systems. LRHR first defines novel representations for both users and items so that they can be content-comparable. Then, LRHR identifies a set of novel meta-level features for learning purposes. Finally, LRHR adopts RankSVM, a pairwise learning to rank algorithm, to generate recommendation lists of items for users. Extensive experiments on benchmarks in comparison with the state-of-the-art algorithms demonstrate the performance gain of our approach.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809015237/http://users.jyu.fi/~swang/publications/CIKM12-LR4RS.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6e/7c/6e7c188304d65d5bbc9ba5b5e0336eb03cf21daa.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2396761.2398610"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>