Recent Advances in Recommender Systems and Future Directions
This article presents an overview of recent methodological advances in developing nearest-neighbor-based recommender systems that have substantially improved their performance. The key components in these methods are: (i) the use of statistical learning to estimate from the data the desired user-user and item-item similarity matrices, (ii) the use of lower-dimensional representations to handle issues associated with data sparsity, (iii) the combination of neighborhood and latent space models, and (iv) the direct incorporation of auxiliary information during model estimation. The article will also provide illustrative examples for these methods in the context of item-item nearest-neighbor methods for rating prediction and Top-N recommendation. In addition, the article will present an overview of exciting new application areas of recommender systems along with the challenges and opportunities associated with them.
Recommender systems  are designed to identify the items that a user will like or find useful based on the user’s prior preferences and activities. These systems have become ubiquitous and are an essential tool for information filtering and (e-)commerce . Over the years, collaborative filtering (CF) , which derives these recommendations by leveraging past activities of groups of users, has emerged as the most prominent approach in recommender systems. Among the multitude of CF methods that have been developed, user- and item-based nearest-neighbor approaches are the simplest to understand and are easy to extend to capture different user behavioral models and types of available information. However, in their classical forms [3, 4, 5, 6, 7, 8], the performance of these methods is worse than that of latent-space based approaches [9, 10, 11, 12, 13, 14].
In this article, we present an overview of recent methodological advances in developing nearest-neighbor-based CF methods for recommender systems that have substantially improved their performance. In specific, we overview the methods that (i) use statistical learning to estimate from the data the desired user-user and item-item similarity matrices, (ii) use lower-dimensional representations to handle issues associated with sparsity, (iii) combine neighborhood and latent space models, and (iv) directly incorporate auxiliary information during model estimation. We provide illustrative examples for these methods in the context of item-item nearest-neighbor methods for rating prediction and Top-N recommendation. We also briefly discuss the reasons as to why such methods achieve superior performance and derive insights from there for further development. In addition, we present an overview of exciting new application areas of recommender systems along with the challenges and opportunities associated with them.
2 Review of Previous Research
In the conventional nearest-neighbor-based CF methods [3, 4, 5, 6, 7, 8, 15, 16, 17], the user-item ratings stored in the system are directly used to predict ratings or preferences for a user on certain items. This has been done in two ways known as user-based recommendation and item-based recommendation. In user-based recommendation methods such as those used in GroupLens , Bellcore video  and Ringo , a set of nearest user neighbors for a target user is first identified as the users who have most similar preference patterns as the target user over a set of common items. Then the preferences from such neighboring users on a certain item are leveraged to produce a recommendation score of that item to the target user. In item-based approaches [3, 5, 7], on the other hand, a set of nearest item neighbors for a certain item is first identified as those that have been preferred in a most similar fashion as the item of interest by a set of common users. Then the recommendation score of the item to a user is generated by incorporating the user’s preferences on the neighboring items.
The fact that the conventional nearest-neighbor-based CF methods work well in practice is largely due to that the available information of user-item preferences is typically very sparse, but such CF methods can capture and utilize the most important signals among the sparse data using simplistic and non-parametric approaches. Nearest-neighbor-based CF methods are intuitive, easy in computation, and very scalable to large e-commerce datasets and thus suitable for really applications. Although there have been numerous other recommendation methods developed over the years, particularly the latent-space-based methods [9, 10, 11, 12, 13, 14], which involve more complicated modeling, demand much more computational resources and could achieve better recommendation performance, nearest-neighbor-based CF methods still remain as a strong baseline particularly when the trade-off between computational costs and performance is a major consideration.
3 CF from Data-Driven Nearest Item Neighbors: SLIM
4 CF from Factorized Item Similarities: FISM
5 CF from User-Specific Feature-Based Similarities: UFSM
UFSM and FBSM calculate item-item similarities only from item features. This characteristics enables them to conduct cold-start recommendations for new items when there is no existing rating information for the new items. As demonstrated in  and , the performance of UFSM and FBSM for cold-start recommendations is superior to that of the state-of-the-art methods.
A different way to leverage auxiliary information is to use such information to bias the learning of an existing CF method. For example, SLIM is extended to incorporate item features in such a way . In the collective SLIM method (cSLIM), it is imposed that the item-item similarities calculated from user-item information and the item-item similarities calculated from item features are identical, while in the relaxed collective SLIM (rcSLIM), the item-item similarities calculated from both aspects are close. In these methods, the item features are used to bias the learning of item neighbors so that the neighbor structures conform to and also encode the information from item features. It is demonstrated in  that when the user-item information is sparse, item features can play an important role for CF methods that use such information to achieve good recommendation performance.
6 Future Directions on Nearest-Neighbor-Based CF
There have existed other methods that have substantially improved conventional CF methods. Such methods include the ones that can capture high-order relations among item similarities , the methods that learn and utilize non-linear relations among items , etc. However, to make CF methods fully personalized, highly scalable and sufficiently robust against data sparsity and meanwhile produce high-quality recommendations, significant efforts from recommender system communities have been continuously dedicated. It has been recognized  that items may fall into clusters and thus item-item similarities may have local structures that may be sufficiently different from other local structures and from global structures, which leads to potential future research that discovers and incorporates local item neighbors into conventional CF methods. Fast and scalable learning algorithms are demanded for such methods once non-linear similarity structures are involved. On the other hand, dynamic components (e.g., user preferences change over time) have become ubiquitous among recommender systems, which may result in dynamically evolving user/item neighborhood structures. Such evolvement may exhibit interesting signals from which novel knowledge can be derived and used to predict future user preference/needs and make recommendations correspondingly (e.g., to recommend TV shows, to recommend courses). Another interesting research topic would be to develop scalable and efficient methods that can effectively incorporate heterogeneous auxiliary information from various static/dynamic sources in a systematical way into CF methods.
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