Additive Smoothing for Relevance-Based Language Modelling of Recommender Systems Daniel Valcarce, Javier Parapar, Álvaro Barreiro Information Retrieval Lab Department of Computer Science University of A Coruña, Spain {daniel.valcarce, javierparapar, barreiro}@udc.es ABSTRACT the users' past behaviour. Continuous developments in this The use of Relevance-Based Language Models for top-N rec- field have been made to meet these high expectations. ommendation has become a promising line of research. Pre- We can distinguish multiple approaches to recommenda- vious works have used collection-based smoothing methods tion [24]. They are often classified in three main categories: for this task. However, a recent analysis on RM1 (an estima- content-based, collaborative filtering and hybrid techniques. tion of Relevance-Based Language Models) in document re- Content-based approaches generate recommendations based trieval showed that this type of smoothing methods demote on the item and user descriptions: they suggest items simi- the IDF effect in pseudo-relevance feedback. In this paper, lar to those liked by the target user [9]. In contrast, collab- we claim that the IDF effect from retrieval is closely related orative filtering methods rely on the interactions (typically to the concept of novelty in recommendation. We perform ratings) between users and items in the system [21]. Finally, an axiomatic analysis of the IDF effect on RM2 conclud- there exist hybrid algorithms that combine both collabora- ing that this kind of smoothing methods also demotes the tive filtering and content-based approaches. IDF effect in recommendation. By axiomatic analysis, we Traditionally, Information Retrieval (IR) has focused on find that a collection-agnostic method, Additive smoothing, delivering the information that users demand [1]. On the does not demote this property. Our experiments confirm other hand, Information Filtering (IF) explores ways of se- that this alternative improves the accuracy, novelty and di- lecting relevant pieces of information from a stream of data versity figures of the recommendations. [12]. Recommender systems are active information filters: these methods learn from the users' behaviour providing per- sonalised suggestions. Given the similarities between IR and CCS Concepts IF, some authors have considered them to be sibling fields •Human-centered computing ! Collaborative filter- or two sides of the same coin [2]. The main difference be- ing; •Information systems ! Recommender systems; Lan- tween these fields is the way in which the information need guage models; is obtained. Information Retrieval systems usually receive a query prompted by the user meanwhile Information Filter- ing systems infer the users' needs. Keywords Due to the closeness of IR and IF, exploiting Information Recommender systems, Relevance-Based Language Models, Retrieval methods for recommenders systems has become collaborative filtering a fertile area of research [31, 4, 23, 27, 30]. In particu- lar, in this paper we want to further investigate the use of Relevance-Based Language Models for recommendation. 1. INTRODUCTION Lavrenko and Croft [17] devised the Relevance-Based Lan- Recommender systems are becoming increasingly popu- guage Modelling framework for the pseudo-relevance feed- lar these days. The enormous growth of data available to back task proposing two methods: RM1 and RM2. Later, users has changed the way we access information. Users are Parapar et al. adapted this technique to the collaborative fil- becoming more and more demanding: they are eager to re- tering scenario achieving high figures of precision [23]. For ceive personalised contents instead of explicitly stating their pseudo-relevance feedback, RM1 is the preferred method; information needs. Therefore, the applicability of recom- however, RM2 yields better results than RM1 in top-N rec- mender systems is undeniable. This technology is designed ommendation. for providing relevant items of information by learning from To be effective, language models employ smoothed prob- ability estimates. The selection of the smoothing method Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed is crucial for the performance of language models both in for profit or commercial advantage and that copies bear this notice and the full citation Information Retrieval [33] and in recommendation [29]. A on the first page. Copyrights for components of this work owned by others than the recent study presented an axiomatic analysis of smoothing author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or methods for different pseudo-relevance feedback techniques republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. (including RM1) [13]. The authors concluded that the tra- CERI ’16, June 14 - 16, 2016, Granada, Spain ditional collection-based methods that have been used for c 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM. smoothing language models in the ad hoc retrieval task [33] ISBN 978-1-4503-4141-7/16/06. $15.00 are not suitable for performing pseudo-relevance feedback DOI: http://dx.doi.org/10.1145/2934732.2934737 because they demote the IDF (inverse document frequency) Recommender systems work with a set of users U and a effect. Instead, they proposed the use of RM1 with Ad- set of items I. Collaborative filtering approaches employ ditive smoothing, a collection-agnostic smoothing method. the interactions between users and items to generate recom- This choice is supported by their axiomatic analysis and mendations. This work is devoted to explicit feedback col- outperformed traditional alternatives in their experiments. laborative filtering techniques which are based on ratings. The IDF is a measure of term specificity [26, 25]. We claim A rating from a user u 2 U to an item i 2 I is denoted by that this concept is related to novelty in recommender sys- r(u; i). Additionally, Iu refers to the set of items rated by tems [7]. In this paper, we study the connection between the user u. Finally, the recommendation list for the user u k these two concepts and its implications in recommendation. of length k is represented by Lu. Furthermore, we perform an axiomatic analysis of the IDF effect in RM2 in the context of recommendation. We study 2.2 Relevance-Based Language Models three collection-based smoothing methods (Jelinek-Mercer, Relevance-based Language Models (often abbreviated as Dirichlet Priors and Absolute Discounting) and a collection- Relevance Models or simply RM) are a state-of-the-art tech- agnostic method (Additive smoothing). Our goal is to deter- nique for the pseudo-relevance feedback task in text retrieval mine if the application of Additive smoothing to Relevance- [17]. Nonetheless, this framework has also been adapted to Based Language Models is valuable not only for the pseudo- top-N recommendation with great success [23]. relevance feedback task, but also for top-N recommenda- Pseudo-relevance feedback (PRF) is a way of expanding tion. Our axiomatic analysis proves that the aforemen- queries with new terms in a text retrieval system. Since tioned collection-based smoothing methods demote the IDF a standard retrieval system returns a list of documents ac- effect on RM2 in recommendation as it does on RM1 for cording to the query prompted by the user, the performance pseudo-relevance feedback. Moreover, we find that Additive of this process depends fundamentally on the retrieval algo- smoothing neither promotes nor demotes the IDF effect on rithm and the quality of the user's query [1]. For this reason, RM2. Thus, if the IDF heuristic is valuable for recommenda- expanding this query with relevant terms is a way of im- tion, Additive smoothing should work better than the other proving the outcome of a retrieval system. Pseudo-relevance methods. feedback is an automatic and effective query expansion ap- To verify this last assumption|the utility of the IDF ef- proach. In PRF, we assume that the top documents re- fect in recommendation|we test experimentally the quality trieved with the original query are relevant (they form the of the recommendations generated by RM2 with different pseudo-relevant set). PRF methods expand the initial query smoothing methods. We use accuracy, diversity and novelty with the most relevant terms from the pseudo-relevant set. metrics to cover different aspects of top-N recommendation. Then, the expanded query is used for performing a second Our experiments show that Additive smoothing provides retrieval which provides the results to be presented to the better figures of accuracy, diversity and novelty and, at the user. same time, it is more stable with respect to the smoothing The PRF approach has been adapted to collaborative fil- parameter than the collection-based smoothing methods. tering recommendation [23] in the following way. Instead In summary, the contributions of this paper are (1) an of a query, we have a user whose profile (i.e., the set of investigation of the relationship between the IDF effect in items that the user has rated) has to be expanded with new retrieval and novelty in recommendation, (2) an axiomatic relevant items. For doing so, we use a set of neighbours analysis of the IDF effect of RM2 using the three most or similar users. The intuitive idea is that the candidates popular collection-based smoothing methods and Additive for recommendation are those items that are relevant in the smoothing and (3) an empirical comparison of the four stud- neighbourhood of the user. In this way, users play a dual ied smoothing methods in terms of accuracy, novelty and role: they act as queries when they are the target user of the diversity of the recommendations concluding that Additive recommendation process but they also act as documents of smoothing is the method that provides best figures for these the pseudo-relevant set when they are considered as neigh- metrics.