Recommender  Systems  have  become  a  fundamental  part  of  various  applications supporting users when searching for items they could be interested in,at  a  given  moment.  However,  the  majority  of  Recommender  Systems  generate  isolate  item  recommendations  based  mainly  on  user-item  interactions,  without  taking   into   account   other   important   information   about   the   recommendation   moment, able to deliver users a more complete experience. In this paper, a hybrid Case-based Reasoning model generating recommendations of sets of music items, based  on  the  underlying  structures  found  in  previous  playlists,  is  proposed. Furthermore,  the  described  system  takes  into  account  the  similarity  of  the  basic  contextual information of the current and the past recommendation moments.  The initial evaluation shows that the proposed approach may deliver recommendations of equal and higher accuracy than some of the widely used techniques.Peer ReviewedPostprint (author's final draft

Gkatzioura, Anna

Sànchez-Marrè, Miquel

English

Recommender  Systems  have  become  a  fundamental  part  of  various  applications supporting users when searching for items they could be interested in,at  a  given  moment.  However,  the  majority  of  Recommender  Systems  generate  isolate  item  recommendations  based  mainly  on  user-item  interactions,  without  taking   into   account   other   important   information   about   the   recommendation   moment, able to deliver users a more complete experience. In this paper, a hybrid Case-based Reasoning model generating recommendations of sets of music items, based  on  the  underlying  structures  found  in  previous  playlists,  is  proposed. Furthermore,  the  described  system  takes  into  account  the  similarity  of  the  basic  contextual information of the current and the past recommendation moments.  The initial evaluation shows that the proposed approach may deliver recommendations of equal and higher accuracy than some of the widely used techniques.Peer Reviewe

UPCommons

Using contextual information in music playlist recommendations

UPCommons. Portal del coneixement obert de la UPC

Using Contextual Information in Music Playlist Recommendations Anna GATZIOURAa,1 and Miquel SÀNCHEZ-MARRÈ a a Dept. of Computer Science,  Knowledge Engineering and Machine Learning Group (KEMLG) Universitat Politècnica de Catalunya · BarcelonaTech Abstract. Recommender Systems have become a fundamental part of various applications supporting users when searching for items they could be interested in, at a given moment. However, the majority of Recommender Systems generate isolate item recommendations based mainly on user-item interactions, without taking into account other important information about the recommendation moment, able to deliver users a more complete experience. In this paper, a hybrid Case-based Reasoning model generating recommendations of sets of music items, based on the underlying structures found in previous playlists, is proposed. Furthermore, the described system takes into account the similarity of the basic contextual information of the current and the past recommendation moments.  The initial evaluation shows that the proposed approach may deliver recommendations of equal and higher accuracy than some of the widely used techniques.  Keywords. Hybrid Recommender System, Set of items recommendation, Case-Based Reasoning, Graph-based Similarity, Context, Playlist Recommendations, Music Recommender Systems. 1. Introduction The majority of current recommender systems treat the recommendation problem at two dimensions, namely users and items, capture their interactions through ratings and based on those predict the most relevant items for a specific user. They usually focus on single item recommendations and tend to ignore other information related to the recommendation moment, like time, location, and joint item selections that in some application domains may highly affect the recommendation results. The aim of the implemented system is the automatic generation and recommendation of music playlists, more specific, the recommendation of sets of music items being able to complete a started playlist. Therefore our emphasis is on the item co-occurring patterns and the playlist generation moment rather than on the users that performed those, as users attitudes may vary depending on their actual needs. In addition, apart from the characteristics of each item, the recommended set must have some characteristics as a whole, like variety and coherence, meaning that the same song should not be repeated and the changes between items should be smooth [2][11].                                                            1  Corresponding Author, A. Gatzioura, Dept. of Computer Science, Universitat Politècnica de Catalunya, Campus Nord, C/ Jordi Girona 1-3, 08034, Barcelona, Catalonia; E-mail: gatzioura@cs.upc.edu. In this work, we propose an extended model of our previous MusCBR recommender [7] that first evaluates the contextual similarities of the recommendation moments in order to capture possible special characteristics, and then searches for the most adequate playlists. In the next section we present a short overview of music and playlist recommendation techniques and the term of context, while in the third section the description of our framework can be found. Following the comparison of our model with commonly used techniques and some of our future objectives are presented. 2. Related Work 2.1. Music RSs Music recommender systems have their basis both in fields of recommender systems (RSs) and Music Information Retrieval (MIR) therefore have to deal with the common limitations of both areas [9]. Traditional MIR techniques use content-based (CB) audio related techniques that apart from the general limitations of content-based systems, like overspecialization and limited diversity, require a deeper knowledge of the application domain [4][14]. On the other hand, Collaborative Filtering (CF) techniques focus on user ratings to predict the rating a user would assign to a previously unknown song, based on the behavior of similar users, thus heavily suffer from cold-start and long tail effect limitations [12].  More than recommending single songs, increased focus has been placed lately on the construction and recommendation of music playlists, sets of music items designed to be consumed as a sequence, to enable users in organizing their music libraries and having a more complete experience [13]. However, as there are no solid methods combining user’s perception of music with sound characteristics, it is difficult to specify the exact characteristics that the items of a playlist should have in order to compose a satisfactory result for the listener [11]. The authors in [13] categorize playlist generation algorithms mainly into constraint satisfaction methods that generate playlists based on some user entered criteria, similarity heuristics that given a seed music item and using some similarity function aim to identify the most similar ones, and machine learning approaches that use a set of playlists to train a model and based on it to recommend playlists. Markov models and association rules, or sequential patterns, are also being used to capture song co-occurrences. CF approaches treating playlists as users or CB approaches using musical features can be used. Among the major limitations of these methods are their computational cost and their performance dependency on the used data. Due to the long tail effect in the music domain, popularity-based techniques may also be of high accuracy [3]. 2.2. Context Incorporating contextual parameters into the recommendation problem has been found to improve recommendation accuracy, and possible users’ access to more long tail items [5]. Initially context was defined as any information referring to the user’s location, the people and the resources around him/her and the changes in those. In music recommendations contextual information can be classified mainly into primary (environmental-related) like location, time and weather, and secondary (user-related) like activity and emotional parameters [8].  A context-aware recommendation process refers to the estimation of user contextual characteristics, and based on those, on the generation of the most relevant recommendations, and it generally takes three forms. Contextual pre-filtering selects, according to a specific context, the data that will be further used for the recommendations. In contextual post-filtering an initial recommendation set is generated using the traditional recommendation approaches and is then adjusted based on the actual context. Finally, in contextual modelling contextual information is used to transform items into a different dimension [1]. In order to improve the predictive ability and also support long tail recommendations, Domingues et al. in [6] present an interesting contextual modeling approach that exploits multidimensional data with two dimensional recommenders. The additional dimensions are modeled as “virtual items” that along with the regular items are used for building the recommendation model. On the other hand, Cremonesi et al. [5] propose the use of association rules to identify the most significant correlations among item and contextual characteristics in order to filter the set of predictions generated by a traditional recommendation method. 3. Method Description In this work, we propose an extension of our previous model [7] that first evaluates contextual information in order to better capture the characteristics of playlists generated at a specific moment.   Figure 1. Recommendation process overview. As the scope is to generate recommendation of joint music items that would fit well into a started playlist, we model entire playlists as cases. After a contextual pre-filtering the system follows the general CBR cycle, thus in order to generate recommendations for a new list we first find the most similar past lists in order to build the new solution according to their structures [10]. Finally, in order to generate the recommendation list, our algorithm sorts the items found in the most similar lists based on the aggregated similarity of the cases in which they appeared. 3.1. Contextual Filtering In the implemented system, first the contextual information about the playlists is evaluated through a contextual pre-filtering process in order to first identify a set of candidate cases able to address more complicated queries in terms of user experience, as well as to fasten their retrieval. Among the contextual parameters available about playlists, are the user who constructed them and the time moment at which this happened. After analyzing song distributions in playlists per month and per hour, (based on the hypothesis that music preferences are associated with the weather that changes during the year, and the hour of the day that affects the activities performed), we have found that, indeed, the hour of the day seems to influence the style of the generated playlists. Therefore, the initial contextual clustering is done based on the time that playlists were constructed. When a new playlist is introduced, its context is evaluated and the candidate cases are retrieved from the most similar contextual cluster. 3.2. Candidate Cases  After the contextual pre-filtering we search among the candidate cases for the most similar to the new case. Given a database of z distinct songs (items), 𝐼𝐼 = {𝑖𝑖1, … , 𝑖𝑖𝑧𝑧} and a set of previously reproduced playlists 𝐿𝐿 = {𝑙𝑙1, … , 𝑙𝑙𝑘𝑘}  where each playlist 𝑙𝑙. ∈ 𝐿𝐿  is described as the set of songs from the set I of which it consists, like 𝑙𝑙. = �𝑖𝑖1, 𝑖𝑖2, … , 𝑖𝑖𝑗𝑗�. The purpose of the system is, given a new initiated list  𝑙𝑙𝑖𝑖𝑖𝑖 = {𝑖𝑖𝑖𝑖1, … , 𝑖𝑖𝑖𝑖𝑗𝑗} to find and recommend the set of n music items  𝑙𝑙𝑟𝑟𝑟𝑟𝑟𝑟 = {𝑖𝑖𝑖𝑖𝑖𝑖 , … , 𝑖𝑖𝑖𝑖(𝑖𝑖+𝑖𝑖−1)}  to complete this list. Therefore, we look for the lists, whose global similarity, fulfills the following equation,  𝑙𝑙′ ∈ 𝐿𝐿: ∀𝑙𝑙 ∈ 𝐿𝐿, 𝑙𝑙′ = 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑆𝑆𝑖𝑖𝑎𝑎(𝑙𝑙, 𝑙𝑙𝑖𝑖𝑖𝑖) (1) The global similarity among two playlists is computed as the aggregated pairwise similarity of the songs (being referred to as local similarity) in them. Given two playlists, a new 𝑙𝑙𝑁𝑁 = {𝑖𝑖𝑁𝑁1, … , 𝑖𝑖𝑁𝑁𝑖𝑖}  and a retrieved 𝑙𝑙𝑅𝑅 = {𝑖𝑖𝑅𝑅1, … , 𝑖𝑖𝑅𝑅𝑅𝑅}  with their i-th item being 𝑖𝑖𝑁𝑁𝑖𝑖  and 𝑖𝑖𝑅𝑅𝑖𝑖  correspondingly, that have local similarity 𝑠𝑠𝑖𝑖𝑎𝑎(𝑖𝑖𝑁𝑁𝑖𝑖 , 𝑖𝑖𝑅𝑅𝑖𝑖)  and where 𝑎𝑎𝑎𝑎𝑎𝑎𝑠𝑠𝑖𝑖𝑎𝑎(𝑖𝑖𝑁𝑁𝑖𝑖 , 𝑖𝑖𝑅𝑅𝑅𝑅) is the maximum local similarity among 𝑖𝑖𝑁𝑁𝑖𝑖 and all the songs in the retrieved list, the global similarity 𝑆𝑆𝑖𝑖𝑎𝑎(𝑙𝑙𝑁𝑁 , 𝑙𝑙𝑅𝑅), of these lists is calculated as, 𝑆𝑆𝑖𝑖𝑎𝑎(𝑙𝑙𝑁𝑁 , 𝑙𝑙𝑅𝑅) = ∑ 𝑎𝑎𝑎𝑎𝑎𝑎𝑠𝑠𝑖𝑖𝑎𝑎(𝑖𝑖𝑁𝑁𝑖𝑖 , 𝑖𝑖𝑅𝑅𝑅𝑅)𝑖𝑖𝑖𝑖=1 /𝑛𝑛𝑁𝑁 (2) 3.3. Item Descriptions Every song can be treated as the node of a graph connected with edges to a set of metadata expressing its characteristics (category, tempo, lyrics language, artist etc.), thus it can be represented as 𝑖𝑖 = {𝑡𝑡1, 𝑡𝑡2, … , 𝑡𝑡𝑖𝑖} , 𝑡𝑡𝑖𝑖 ∈ 𝑇𝑇 . Depending on the level of abstraction that we would like to use, the quantity of item characteristics used in the item description may vary and we may refer as music items to songs, artists, or song styles. Songs are connected through the tags they have in common and the playlists of which they form part. As our scope is to capture the specifications of the songs selected together more than the exact songs, instead of looking for the songs that maximize (1) we focus on labeled styles or artists as this information is more abstract but specific enough to capture the tendencies in playlists.  Given two songs 𝑖𝑖𝑎𝑎  and 𝑖𝑖𝑏𝑏  with 𝑛𝑛(𝑎𝑎 ∩ 𝑏𝑏) number of characteristics in common, 𝑛𝑛(𝑎𝑎\𝑏𝑏) tags associated only with 𝑖𝑖𝑎𝑎 and 𝑛𝑛(𝑏𝑏\𝑎𝑎) associated only with 𝑖𝑖𝑏𝑏 , their (local) similarity is calculated based on the density of their common characteristics like,   𝑠𝑠𝑖𝑖𝑎𝑎(𝑖𝑖𝑎𝑎, 𝑖𝑖𝑏𝑏) =  1 − 𝑙𝑙𝑙𝑙𝑎𝑎2 �1 +𝑖𝑖(𝑎𝑎\𝑏𝑏)+𝑖𝑖(𝑏𝑏\𝑎𝑎)𝑖𝑖(𝑎𝑎∩𝑏𝑏)+𝑖𝑖(𝑎𝑎\𝑏𝑏)+𝑖𝑖(𝑏𝑏\𝑎𝑎)� (3) 4. Evaluation In order to evaluate the performance of the proposed framework we used a personal database with more than 3000 songs and playlists made of them, divided into a training (80%) and a testing part. Further, from the testing lists, we hide a number of items and evaluate the ability of the various methods to identify those items. We focused on the recommendations of item clusters, items having the same style, artist or both, as many times users select only few items from a cluster with specific characteristics simply because they are not aware of the rest.  In Tables 1-3 we present the average precision values (the number of correct recommendations, over the total number of recommendations) achieved by CMusCBR, our contextual recommender, MusCBR, the hybrid recommender in [7], as well as ARs-based (AR), Latent Dirichlet Allocation (LDA), two Popularity-based approaches, based on users’ favourite artists (popA) and styles (popSt), Collaborative Filtering (CF) and Content-based (CB). As it can be seen, the proposed methodology performs better than the compared methods for all the levels of abstraction tested.  Table1: Average precision for the recommendation of complete music styles List length 4 6 8 10 12 AR 0.055 0.063 0.052 0.052 0.035 LDA 0.049 0.061 0.068 0.081 0.094 PopA 0.039 0.054 0.058 0.067 0.064 PopSt 0.043 0.048 0.053 0.064 0.064 CB 0.042 0.050 0.055 0.061 0.056 CF 0.045 0.058 0.068 0.08 0.079 MusCBR 0.078 0.078 0.076 0.086 0.083 CMusCBR 0.097 0.090 0.090 0.096 0.098  Table2: Average precision for artist recommendations List length 4 6 8 10 12 AR 0.052 0.087 0.108 0.115 0.096 LDA 0.034 0.050 0.075 0.087 0.093 PopA 0.044 0.073 0.098 0.115 0.117 CB 0.029 0.045 0.059 0.066 0.072 CF 0.048 0.078 0.102 0.118 0.119 MusCBR 0.095 0.103 0.115 0.127 0.131 CMusCBR 0.096 0.108 0.124 0.139 0.135  Table3: Average precision for the recommendation of music styles List length 4 6 8 10 12 AR 0.261 0.32 0.363 0.367 0.372 LDA 0.202 0.345 0.167 0.264 0.317 PopA 0.34 0.346 0.336 0.334 0.337 PopSt 0.38 0.402 0.394 0.394 0.395 CB 0.289 0.207 0.156 0.122 0.082 CF 0.185 0.259 0.292 0.315 0.345 MusCBR 0.386 0.39 0.397 0.396 0.395 CMusCBR 0.392 0.403 0.4 0.401 0.401 5. Conclusions In this paper, a hybrid CBR approach for music playlists generation and recommendations has been presented. The implemented system first evaluates the context of the recommendation moment, and then, it identifies and recommends sets of music items that are more probable to fit into a started playlist. In order to overcome the semantic gap of music recommender systems, the proposed system combines Case-Based Reasoning with a graph-based model that connects songs through the tags that define their style and the playlists that they form part of, in order to capture their similarity and co-occurring patterns. The initial experimentations have shown that adding a contextual pre-filtering based on the playlists’ creation time leads to improved recommendations’ accuracy and computational performance when compared to our previous and various commonly used methods.  Our intention is to extend this framework in order to provide additional support to long tail recommendations, as items’ novelty and diversity that are considered of high importance for the user experience. In addition, the possible incorporation of more contextual parameters is being evaluated. Finally, testing on large scale data is planned to take place in the near future. References [1] G. Adomavicius, and A. Tuzhilin, “Context-aware recommender systems,” Recommender systems handbook, pp. 191-226, Springer US, 2015. [2] C. Baccigalupo and E. Plaza, “Case-Based Sequential Ordering of Songs for Playlist Recommendation,” Advances in Case-Based Reasoning 4106, p. 286-300, 2006.  [3] G. Bonnin, and D. Jannach, “A comparison of playlist generation strategies for music recommendation and a new baseline scheme,” Workshops at the 27th AAAI Conference on Artificial Intelligence, 2013. [4] M. A. Casey, R. Veltkamp, M. Goto, M. Leman, C. Rhodes, and M. Slaney, “Content-Based Music Information Retrieval: Current Directions and Future Challenges,” Proceedings of the IEEE, 96. pp. 668 – 696, 2008. [5] P. Cremonesi, P. Garza, E. Quintarelli, and R. Turrin, “Top-n recommendations on unpopular items with contextual knowledge,” In 2011 Workshop on Context-aware Recommender Systems, Chicago, 2011. [6] M. A. Domingues, A.  M. Jorge, and C. Soares, “Using contextual information as virtual items on top-n recommender systems,” arXiv preprint arXiv: 1111.2948, 2011. [7] A. Gatzioura, and M. Sànchez-Marrè, “A Case-Based Reasoning Framework for Music Playlist Recommendations,” Procc. of the 4th IEEE International Conference on Control, Decision and Information Technologies (CoDIT’17), April 2017. [8] M. Kaminskas, and F. Ricci, “Contextual Music Information Retrieval and Recommendation: State of the Art and Challenges,” Computer Science Review 6, pp. 89-119, 2012. [9] E. Y. Kim, M. E. Schmidt, R. Migneco, G. B. Morton, P. Richardson, J. Scott, A. J. Speck and D. Turnbull, “Music Emotion Recognition: A State of the Art Review,” Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), pp. 255-266, 2010. [10] R. López de Mántaras, “Case-Based Reasoning,” Machine Learning and its Application, vol. 2049, pp.127-145, 2001. [11] F. Maillet, D. Eck, G. Desjardins, and P. Lamere, “Steerable Playlist Generation by Learning Song Similarity from Radio Station Playlists,” Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009), pp. 345-350, 2009. [12] F. Ricci, L. Rokach, and B. Shapira, “Introduction to recommender systems handbook,” pp. 1-35, Springer US, 2011. [13] M. Schedl, P. Knees, B. McFee, D.  Bogdanov, and M. Kaminskas, “Music recommender systems,” Recommender Systems Handbook, pp. 453-492, Springer US, 2015. [14] B. Shao, D. Wang, T. Li and M. Ogihara, “Music Recommendation Based on Acoustic Features and User Access Patterns,” IEEE Transactions on Audio, Speech, and Language Processing 17:1602 – 1611, 2009. 

https://upcommons.upc.edu/bitstream/handle/2117/132881/Gatzioura%26S%c3%a0nchez-Marr%c3%a8-CCIA17-revised.pdf?sequence=1&isAllowed=y

Using contextual information in music playlist recommendations

Abstract

Similar works

Full text

Available Versions

UPCommons

UPCommons. Portal del coneixement obert de la UPC