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Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social …

Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user’s content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user’s situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.

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- 1. A Contextual-bandit Algorithm for Mobile Context- Aware Recommender System Djallel Bouneffouf, Amel Bouzeghoub & Alda Lopes Gançarski Department of Computer Science, Télécom SudParis, UMR CNRS Samovar, 91011 Evry Cedex, France{Djallel.Bouneffouf, Amel.Bouzeghoub, Alda.Gancarski}@it- sudparis.eu Abstract. Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user’s content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user’s situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms. Keywords: recommender system; machine learning; exploration/exploitation dilemma; artificial intelligence.1 IntroductionMobile technologies have made access to a huge collection of information, anywhereand anytime. In particular, most professional mobile users acquire and maintain alarge amount of content in their repository. Moreover, the content of such repositorychanges dynamically, undergoes frequent insertions and deletions. In this sense, rec-ommender systems must promptly identify the importance of new documents, whileadapting to the fading value of old documents. In such a setting, it is crucial to identi-fy interesting content for users. This problem has been addressed in recent research inthe Mobile Context-Aware Recommender Systems (MCRS) area [2, 4, 5, 14, 19, 20].Most of these approaches are based on the user computational behavior and his sur-rounding environment. Nevertheless, they do not tackle the dynamicity of the user’scontent problem. The bandit algorithm is a well-known solution that addresses thisproblem as a need for balancing exploration/exploitation (exr/exp) tradeoff. A banditalgorithm B exploits its past experience to select documents that appear more fre-quently. Besides, these seemingly optimal documents may in fact be suboptimal, be-cause of the imprecision in B’s knowledge. In order to avoid this undesired case, Badfa, p. 1, 2011.© Springer-Verlag Berlin Heidelberg 2011
- 2. has to explore documents by choosing seemingly suboptimal documents so as togather more information about them. Exploitation can decrease short-term user’s sat-isfaction since some suboptimal documents may be chosen. However, obtaining in-formation about the documents’ average rewards (i.e., exploration) can refine B’sestimate of the documents’ rewards and in turn increases long-term user’s satisfac-tion. Clearly, neither a purely exploring nor a purely exploiting algorithm works well,and a good tradeoff is needed. One classical solution to the multi-armed bandit prob-lem is the ε-greedy strategy [12]. With the probability 1-ε, this algorithm chooses thebest documents based on current knowledge; and with the probability ε, it uniformlychooses any other documents uniformly. The ε parameter controls essentially theexp/exr tradeoﬀ between exploitation and exploration. One drawback of this algo-rithm is that it is difficult to decide in advance the optimal value. Instead, we intro-duce an algorithm named Contextual-ε-greedy that achieves this goal by balancingadaptively the exp/exr tradeoﬀ according to the user’s situation. This algorithm ex-tends the ε-greedy strategy with an update of the exr/exp-tradeoff by selecting suitableuser’s situations for either exploration or exploitation. The rest of the paper is organized as follows. Section 2 gives the key notions usedthroughout this paper. Section 3 reviews some related works. Section 4 presents ourMCRS model and describes the algorithms involved in the proposed approach. Theexperimental evaluation is illustrated in Section 5. The last section concludes the pa-per and points out possible directions for future work.2 Key Notions In this section, we briefly sketch the key notions that will be of use in this paper.The user’s model: The user’s model is structured as a case based, which is composedof a set of situations with their corresponding user’s preferences, denoted U = {(S i;UPi)}, where Si is the user’s situation and UPi its corresponding user’s preferences.The user’s preferences: The user’s preferences are deduced during the user’s naviga-tion activities, for example the number of clicks on the visited documents or the timespent on a document. Let UP be the preferences submitted by a specific user in thesystem at a given situation. Each document in UP is represented as a single vectord=(c1,...,cn), where ci (i=1, .., n) is the value of a component characterizing the prefer-ences of d. We consider the following components: the total number of clicks on d,the total time spent reading d and the number of times d was recommended.Context: A user’s context C is a multi-ontology representation where each ontologycorresponds to a context dimension C=(OLocation, OTime, OSocial). Each dimension mod-els and manages a context information type. We focus on these three dimensions sincethey cover all needed information. These ontologies are described in [1, 16].Situation: A situation is an instantiation of the user’s context. We consider a situationas a triple S = (OLocation.xi, OTime.xj, OSocial.xk) where xi, xj and xk are ontology conceptsor instances. Suppose the following data are sensed from the user’s mobile phone: theGPS shows the latitude and longitude of a point "48.89, 2.23"; the local time is"Oct_3_12:10_2012" and the calendar states "meeting with Paul Gerard". The corre-sponding situation is: S=("48.89,2.23","Oct_3_12:10_2012","Paul_Gerard"). To build
- 3. a more abstracted situation, we interpret the user’s behavior from this low-level multi-modal sensor data using ontologies reasoning means. For example, from S, we obtainthe following situation: Meeting=(Restaurant, Work_day, Financial_client).Among the set of captured situations, some of them are characterized as High-LevelCritical Situations.High-Level Critical Situations (HLCS): A HLCS is a class of situations where theuser needs the best information that can be recommended by the system, for instance,during a professional meeting. In such a situation, the system must exclusively performexploitation rather than exploration-oriented learning. In the other case, where the useris for instance using his/her information system at home, on vacation with friends, thesystem can make some exploration by recommending some information ignoringhis/her interest. The HLCS are predefined by the domain expert. In our case we con-duct the study with professional mobile users, which is described in detail in Section 5.As examples of HLCS, we can find S1 = (restaurant, midday, client) or S2= (company,morning, manager).3 Related WorkWe refer, in the following, recent recommendation techniques that tackle the problemof making dynamic exr/exp (bandit algorithms). Existing works considering the user’ssituation in recommendation are not considered in this section, refer to [1] for furtherinformation.Very frequently used in reinforcement learning to study the exr/exp tradeoff, the mul-ti-armed bandit problem was originally described by Robbins [11]. The ε-greedy isone of the most used strategy to solve the bandit problem and was first described in[10]. The ε-greedy strategy chooses a random document with epsilon-frequency (ε),and chooses the document with the highest estimated mean otherwise. The estimationis based on the rewards observed thus far. ε must be in the interval [0, 1] and itschoice is left to the user. The first variant of the ε-greedy strategy is what [6, 10] referto as the ε-beginning strategy. This strategy makes exploration all at once at the be-ginning. For a given number I of iterations, documents are randomly pulled during theεI first iterations; during the remaining (1−ε)I iterations, the document of highestestimated mean is pulled. Another variant of the ε-greedy strategy is what [10] callsthe ε-decreasing. In this strategy, the document with the highest estimated mean isalways pulled except when a random document is pulled instead with εi frequency,where εi = {ε0/ i}, ε0 ∈]0,1] and i is the index of the current round. Besides ε-decreasing, four other strategies presented [3]. Those strategies are not described herebecause the experiments done by [3] seem to show that ε-decreasing is always asgood as the other strategies. Compared to the standard multi-armed bandit problemwith a ﬁxed set of possible actions, in MCRS, old documents may expire and newdocuments may frequently emerge. Therefore it may not be desirable to perform theexploration all at once at the beginning as in [6] or to decrease monotonically theeﬀort on exploration as the decreasing strategy in [10].As far as we know, no existing works address the problem of exr/exp tradeoff inMCRS. However few research works are dedicated to study the contextual banditproblem on recommender systems, where they consider the user’s behavior as the
- 4. context of the bandit problem. In [13], the authors extend the ε-greedy strategy bydynamically updating the ε exploration value. At each iteration, they run a samplingprocedure to select a new ε from a ﬁnite set of candidates. The probabilities associat-ed to the candidates are uniformly initialized and updated with the ExponentiatedGradient (EG) [7]. This updating rule increases the probability of a candidate ε if itleads to a user’s click. Compared to both ε-beginning and ε-decreasing, this techniquegives better results. In [9], authors model the recommendation as a contextual banditproblem. They propose an approach in which a learning algorithm sequentially selectsdocuments to serve users based on their behavior. To maximize the total number ofuser’s clicks, this work proposes LINUCB algorithm that is computationally efficient. As shown above, none of the mentioned works tackles both problems of exr/expdynamicity and user’s situation consideration in the exr/exp strategy. This is preciselywhat we intend to do with our approach. Our intuition is that, considering the criticali-ty of the situation when managing the exr/exp-tradeoff, improves the result of theMCRS. This strategy achieves high exploration when the current user’s situation isnot critical and achieves high exploitation in the inverse case.4 MCRS ModelIn our recommender system, the recommendation of documents is modeled as a con-textual bandit problem including user’s situation information [8]. Formally, a banditalgorithm proceeds in discrete trials t = 1…T. For each trial t, the algorithm performsthe following tasks:Task 1: Let S t be the current user’s situation, and PS the set of past situations.The system compares S t with the situations in PS in order to choose the mostsimilar one, S p: S p= arg max sim( S t , S c ) (1) Sc PSThe semantic similarity metric is computed by: sim(S t ,S c ) = j sim j x tj ,x c j (2) jIn Eq.2, simj is the similarity metric related to dimension j between two conceptsxjt and xjc; αj is the weight associated to dimension j (during the experimentalphase, αj has a value of 1 for all dimensions). This similarity depends on howclosely xj c and xjc are related in the corresponding ontology. We use the samesimilarity measure as [15, 17, 18] defined by: sim j x tj , x c 2 deph( LCS ) (3) (deph( x c ) deph( x tj )) j j In Eq. 3, LCS is the Least Common Subsumer of xjt and xjc, and deph is thenumber of nodes in the path from the node to the ontology root.Task 2: Let D be the document collection and Dp D the set of documents rec-
- 5. ommended in situation S p. After retrieving S p, the system observes the user’sbehavior when reading each document d p Dp. Based on observed rewards, thealgorithm chooses document d p with the greater reward r p.Task 3: After receiving the user ’s reward, the algorithm improves its document-selection strategy with the new observation: in situation S t, document d p obtainsa reward rt. When a document is presented to the user and this one selects it by a click, areward of 1 is incurred; otherwise, the reward is 0. The reward of a document isprecisely its Click Through Rate (CTR). The CTR is the average number ofclicks on a document by recommendation.4.1 The ε-greedy algorithmThe ε-greedy algorithm recommends a predefined number of documents N selectedusing the following equation: di argmax UC ( getCTR( d )) Random(UC ) if ( q ) otherwise (4) In Eq. 4, i∈{1,…N}, UC={d1,…,dP} is the set of documents corresponding to theuser’s preferences; getCTR() computes the CTR of a given document; Random() re-turns a random element from a given set, allowing to perform exploration; q is a ran-dom value uniformly distributed over [0, 1] which defines the exr/exp tradeoff; ε isthe probability of recommending a random exploratory document.4.2 T h e contextual-ε-greedy algorithmTo improve the adaptation of the ε-greedy algorithm to HLCS situations, thecontextual-ε-greedy algorithm compares the current user’s situation St with the HLCSclass of situations. Depending on the similarity between the St and its most similarsituation Sm ∈ HLCS, being B the similarity threshold (this metric is discussed below),two scenarios are possible:(1) If sim(St, Sm) ≥ B, the current situation is critical; the ε-greedy algorithm is usedwith ε=0 (exploitation) and St is inserted in the HLCS class of situations.(2) If sim(St, Sm) < B, the current situation is not critical; the ε-greedy algorithm isused with ε>0 (exploration) computed as indicated in Eq.5. sim( S t , S m ) 1 if ( sim( S t , S m )) B B (5) 0 otherwiseTo summarize, the system does not make exploration when the current user’s situa-tion is critical; otherwise, the system performs exploration. In this case, the degree ofexploration decreases when the similarity between St and Sm increases.
- 6. 5 Experimental EvaluationIn order to empirically evaluate the performance of our approach, and in the absenceof a standard evaluation framework, we propose an evaluation framework based on adiary set of study entries. The main objectives of the experimental evaluation are: (1)to find the optimal threshold B value described in Section 4.2 and (2) to evaluate theperformance of the proposed algorithm (contextual-ε-greedy). In the following, wedescribe our experimental datasets and then present and discuss the obtained results. We have conducted a diary study with the collaboration of the French softwarecompany Nomalys1. This company provides a history application, which records thetime, current location, social and navigation information of its users during their ap-plication use. The diary study has taken 18 months and has generated 178 369 diarysituation entries. Each diary situation entry represents the capture, of contextual time,location and social information. For each entry, the captured data are replaced withmore abstracted information using time, spatial and social ontologies [1]. From thediary study, we have obtained a total of 2 759 283 entries concerning the user’s navi-gation, expressed with an average of 15.47 entries per situation. In order to set out the threshold similarity value, we use a manual classification asa baseline and compare it with the results obtained by our technique. So, we take arandom sampling of 10% of the situation entries, and we manually group similar situ-ations; then we compare the constructed groups with the results obtained by our simi-larity algorithm, with different threshold values. Fig. 1. Effect of B threshold value on the similarity precisionFig. 1 shows the effect of varying the threshold situation similarity parameter B in theinterval [0, 3] on the overall precision. Results show that the best performance is ob-tained when B has the value 2.4 achieving a precision of 0.849. Consequently, we usethe optimal threshold value B = 2.4 for testing our MCRS. To test the proposed contextual-ε-greedy algorithm, we firstly have collected 3000situations with an occurrence greater than 100 to be statistically meaningful. Then, we1 Nomalys is a company that provides a graphical application on Smartphones allowing users to access their company’s data.
- 7. have sampled 10000 documents that have been shown on any of these situations. Thetesting step consists of evaluating the algorithms for each testing situation using theaverage CTR. The average CTR for a particular iteration is the ratio between the totalnumber of clicks and the total number of displays. Then, we calculate the averageCTR over every 1000 iterations. The number of documents (N) returned by the rec-ommender system for each situation is 10 and we have run the simulation until thenumber of iterations reaches 10000, which is the number of iterations where all algo-rithms have converged. In the first experiment, in addition to a pure exploitation base-line, we have compared our algorithm to the algorithms described in the related work(Section 3): ε-greedy; ε-beginning, ε-decreasing and EG. In Fig. 2, the horizontal axisis the number of iterations and the vertical axis is the performance metric. Fig. 2. Average CTR for exr/exp algorithmsWe have parameterized the different algorithms as follows: ε-greedy was tested withtwo parameter values: 0.5 and 0.9; ε-decreasing and EG use the same set {εi = 1- 0.01* i, i = 1,...,100}; ε-decreasing starts using the highest value and reduces it by 0.01every 100 iterations, until it reaches the smallest value. Overall tested algorithmshave better performance than the baseline. However, for the first 2000 iterations, withpure exploitation, the exploitation baseline achieves a faster increase convergence.But in the long run, all exr/exp algorithms improve the average CTR at convergence.We have several observations regarding the different exr/exp algorithms. For the ε-decreasing algorithm, the converged average CTR increases as the ε decreases (ex-ploitation augments). For the ε-greedy(0.9) and ε-greedy(0.5), even after conver-gence, the algorithms still give respectively 90% and 50% of the opportunities todocuments having low average CTR, which decreases significantly their results.While the EG algorithm converges to a higher average CTR, its overall performanceis not as good as ε-decreasing. Its average CTR is low at the early step because ofmore exploration, but does not converge faster. The contextual-ε-greedy algorithmeffectively learns the optimal ε; it has the best convergence rate, increases the averageCTR by a factor of 2 over the baseline and outperforms all other exr/exp algorithms.The improvement comes from a dynamic tradeoff between exr/exp, controlled by thecritical situation (HLCS) estimation. At the early stage, this algorithm takes full ad-vantage of exploration without wasting opportunities to establish good results.
- 8. 6 ConclusionIn this paper, we study the problem of exploitation and exploration in mobile context-aware recommender systems and propose a novel approach that balances adaptivelyexr/exp regarding the user’s situation. In order to evaluate the performance of theproposed algorithm, we compare it with other standard exr/exp strategies. The exper-imental results demonstrate that our algorithm performs better on average CTR invarious configurations. In the future, we plan to evaluate the scalability of the algo-rithm on-board a mobile device and investigate other public benchmarks.References 1. Bouneffouf, D., Bouzeghoub A. & Gançarski A. L.: Following the User’s Interests in Mo- bile Context-Aware recommender systems. AINA Workshops, pp. 657-662. Fukuoka, Ja- pan (2012). 2. Adomavicius, G., Mobasher, B., Ricci, F. Alexander T.: Context-Aware Recommend- er Systems. AI Magazine. vol. 32(3), pp. 67-80 (2011). 3. Auer, P., Cesa-Bianchi, N., and Fischer, P.: Finite Time Analysis of the Multiarmed Ban- dit Problem. Machine Learning, vol 2, pp. 235–256. Kluwer Academic Publishers, Hing- ham (2002). 4. Baltrunas, L., Ludwig, B., Peer, S., and Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing, pp.1-20, Springer, London (2011). 5. Bellotti, V., Begole, B., Chi, E.H., Ducheneaut, N., Fang, J., Isaacs, E.: Activity- Based Serendipitous Recommendations with the Magitti Mobile Leisure Guide. Pro- ceedings on the Move. pp. 1157-1166. ACM, New York (2008). 6. Even-Dar, E., Mannor, S., and Mansour, Y.: PAC Bounds for Multi-Armed Bandit and Markov Decision Processes. In Fifteenth Annual Conference on Computational Learning Theory, 255–270 (2002). 7. Kivinen, J., and Manfred, K.: Warmuth. Exponentiated gradient versus gradient de- scent for linear predictors. Information and Computation, vol 132. (1995). 8. Langford, J., and Zhang, T.: The epoch-greedy algorithm for contextual multi-armed ban- dits. In Advances in Neural Information Processing Systems (2008). 9. Lihong, Li., Wei, C., Langford, J.: A Contextual-Bandit Approach to Personalized News Document Recommendation. Proceedings of the 19th international conference on World wide web, app. 661-670, ACM, New York (2010).10. Mannor, S., and Tsitsiklis, J. N.: The Sample Complexity of Exploration in the Mul- ti-Armed Bandit Problem. Computational Learning Theory, app. 255-270 (2003).11. Robbins, H.: Some aspects of the sequential design of experiments. Bulletin of the Ameri- can Mathematical Society. Vol 58, pp.527-535 (1952).12. Watkins, C.: Learning from Delayed Rewards. Ph.D. thesis. Cambridge University (1989).13. Wei, Li., Wang, X., Zhang, R., Cui, Y., Mao, J., Jin, R.: Exploitation and Exploration in a Performance based Contextual Advertising System. Proceedings of the Interna- tional Conference on Knowledge discovery and data mining: pp. 27-36. ACM, New York (2010).14. Sohn, T., Li, K. A., Griswold, W. G., Hollan, J. D.: A Diary Study of Mobile Information Needs. Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing, pp. 433-442, ACM, Florence (2008).
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