Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> Item Ranking: Pairwise Learning and Listwise Learning <s> We address the problem of learning large complex ranking functions. Most IR applications use evaluation metrics that depend only upon the ranks of documents. However, most ranking functions generate document scores, which are sorted to produce a ranking. Hence IR metrics are innately non-smooth with respect to the scores, due to the sort. Unfortunately, many machine learning algorithms require the gradient of a training objective in order to perform the optimization of the model parameters,and because IR metrics are non-smooth,we need to find a smooth proxy objective that can be used for training. We present a new family of training objectives that are derived from the rank distributions of documents, induced by smoothed scores. We call this approach SoftRank. We focus on a smoothed approximation to Normalized Discounted Cumulative Gain (NDCG), called SoftNDCG and we compare it with three other training objectives in the recent literature. We present two main results. First, SoftRank yields a very good way of optimizing NDCG. Second, we show that it is possible to achieve state of the art test set NDCG results by optimizing a soft NDCG objective on the training set with a different discount function <s> BIB001 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> Item Ranking: Pairwise Learning and Listwise Learning <s> In this paper we tackle the problem of recommendation in the scenarios with binary relevance data, when only a few (k) items are recommended to individual users. Past work on Collaborative Filtering (CF) has either not addressed the ranking problem for binary relevance datasets, or not specifically focused on improving top-k recommendations. To solve the problem we propose a new CF approach, Collaborative Less-is-More Filtering (CLiMF). In CLiMF the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR), which is a well-known information retrieval metric for measuring the performance of top-k recommendations. We achieve linear computational complexity by introducing a lower bound of the smoothed reciprocal rank metric. Experiments on two social network datasets demonstrate the effectiveness and the scalability of CLiMF, and show that CLiMF significantly outperforms a naive baseline and two state-of-the-art CF methods. <s> BIB002
This class of recommendation goal requires a model to correctly rank two items in the training data, even though the model could inaccurately predict the value of a single rating. Since recommender systems are more concerned about item ranking for the same user u than ranking for different users, existing approaches sample item pairs (i, j) where r ui > r uj , given fixed user u (i.e., item i is ranked higher than item j for user u), and then let rating estimate pair (r ui ,r uj ) learn to rank the two items withr ui >r uj . In particular, we can use the sigmoid function sig(x) =  Taking (− log) on objective function (20)  where T is the number of training instances {(u, i, j) | {r ui , r uj } ⊆ δ(R), r ui > r uj }. I(x) ∈ {0, 1} denotes an indicator function whose output is 1 if and only if condition x is judged true. We show the connection between (20) and (21)  In the domain of top-N recommendation, the item order outside top-N ranks is unimportant for recommender systems. Maximizing AUC could fail to recommend items, since AUC gives the same penalty to all items. That is, a recommender system could gain high AUC when it accurately ranks the bottom-N items, but it is not beneficial for real-world recommendation, since a user only pays attention to the top-N items. Listwise evaluation metrics like Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (NDCG), and Mean Average Precision (MAP) are proposed to give different penalty values to item ranking positions. There have been attempts to optimize differential versions of the above metrics, such as CliMF BIB002 , SoftRank BIB001 , and TFMAP . Our observations of the surveyed papers indicate that recommender systems reading binary ratings (section 3.4.2) prefer to optimize an item-ranking objective function. Compared with numerical ratings (section 3.4.1), a single binary rating reveals less information on a user's absolute preference. Pairwise learning methods can capture more information by modeling a user's relative preferences, because the number of rating pairs r ui = 1 > 0 = r uj is more than the number of ratings for each user.