=Paper= {{Paper |id=Vol-1448/paper6 |storemode=property |title=The Continuous Cold-start Problem in e-Commerce Recommender Systems |pdfUrl=https://ceur-ws.org/Vol-1448/paper6.pdf |volume=Vol-1448 |dblpUrl=https://dblp.org/rec/conf/recsys/BernardiKKM15 }} ==The Continuous Cold-start Problem in e-Commerce Recommender Systems== https://ceur-ws.org/Vol-1448/paper6.pdf
                           The Continuous Cold Start Problem
                         in e-Commerce Recommender Systems

                      Lucas Bernardi1 , Jaap Kamps2 , Julia Kiseleva3 , Melanie J.I. Mueller1
           1
               Booking.com, Amsterdam, Netherlands. Email: {lucas.bernardi, melanie.mueller}@booking.com
                        2
                          University of Amsterdam, Amsterdam, Netherlands. Email: kamps@uva.nl
                  3
                    Eindhoven University of Technology, Eindhoven, Netherlands. Email: j.kiseleva@tue.nl


ABSTRACT                                                                approaches have supplemented the original collaborative fil-
Many e-commerce websites use recommender systems to rec-                tering techniques [1].
ommend items to users. When a user or item is new, the                     In the most basic formulation, the task of a recommender
system may fail because not enough information is available             system is to predict ratings for items that have not been
on this user or item. Various solutions to this ‘cold-start             seen by the user. Using these predicted ratings, the system
problem’ have been proposed in the literature. However,                 decides which new items to recommend to the user. Recom-
many real-life e-commerce applications suffer from an aggra-            mender systems base the prediction of unknown ratings on
vated, recurring version of cold-start even for known users or          past or current information about the users and items, such
items, since many users visit the website rarely, change their          as past user ratings, user profiles, item descriptions etc. If
interests over time, or exhibit different personas. This paper          this information is not available for new users or items, the
exposes the Continuous Cold Start (CoCoS) problem and its               recommender system runs into the so-called cold-start prob-
consequences for content- and context-based recommenda-                 lem: It does not know what to recommend until the new,
tion from the viewpoint of typical e-commerce applications,             ‘cold’, user or item has ‘warmed-up’, i.e. until enough in-
illustrated with examples from a major travel recommenda-               formation has been generated to produce recommendations.
tion website, Booking.com.                                              For example, which accomodations should be recommended
                                                                        to someone who visits Booking.com for the first time? If
                                                                        the recommender system is based on which accomodations
General Terms                                                           users have clicked on in the past, the first recommendations
CoCoS: continuous cold start                                            can only be made after the user has clicked on a couple of
                                                                        accomodations on the website.
Keywords                                                                   Several approaches have been proposed and successfully
                                                                        applied to deal with the cold-start problem, such as utiliz-
Recommender systems, continous cold-start problem, indus-               ing baselines for cold users [8], combining collaborative fil-
trial applications                                                      tering with content-based recommenders in hybrid systems
                                                                        [14], eliciting ratings from new users [11], or, more recently,
1.    INTRODUCTION                                                      exploiting the social network of users [6, 15]. In particu-
   Many e-commerce websites are built around serving per-               lar, content-based approaches have been very successful in
sonalized recommendations to users. Amazon.com recom-                   dealing with cold-start problems in collaborative filtering
mends books, Booking.com recommends accommodations,                     [3, 4, 13, 14].
Netflix recommends movies, Reddit recommends news sto-                     These approaches deal explicitly with cold users or items,
ries, etc. Two examples of recommendations of accomoda-                 and provide a ‘fix’ until enough information has been gath-
tions and destinations at Booking.com are shown in Fig-                 ered to apply the core recommender system. Thus, rather
ure 1. This widescale adoption of recommender systems on-               than providing unified recommendations for cold and warm
line, and the challenges faced by industrial applications, have         users, they temporarily bridge the period during which the
been a driving force in the development of recommender                  user or item is ‘cold’ until it is ‘warm’. This can be very
systems. The research area has been expanding since the                 successful in situations in which there are no warm users
first papers on collaborative filtering in the 1990s [12, 16].          [3], or in situations when the warm-up period is short and
Many different recommendation approaches have been de-                  warmed-up users or items stay warm.
veloped since then, in particular content-based and hybrid                 However, in many practical e-commerce applications, users
                                                                        or items remain cold for a long time, and can even ‘cool
                                                                        down’ again, leading to a continuous cold-start (CoCoS). In
                                                                        the example of Booking.com, many users visit and book in-
                                                                        frequently since they go on holiday only once or twice a year,
                                                                        leading to a prolonged cold-start and extreme sparsity of col-
                                                                        laborative filtering matrices, see Fig. 2 (top). In addition,
                                                                        even warm long-term users can cool down as they change
                                                                        their needs over time, e.g. going from booking youth hos-
CBRecSys 2015, September 20, 2015, Vienna, Austria.                     tels for road trips to resorts for family vacations. Such cool-
Copyright remains with the authors and/or original copyright holders.
 Customers who viewed Hotel Sacher Wien also viewed:              Destinations related to Vienna:




Figure 1: Examples of recommender systems on Booking.com. User-to-user collaborative filtering (left):
recommend accomodations viewed by similar users to a user who just looked at ‘Hotel Sacher Wien’. Item-
to-item content-based recommendations (right): recommend destinations similar to a particular destination,
Vienna.


downs can happen more frequently and rapidly for users who         2.2    Item Continuous Cold-Start
book accommodations for different travel purposes, e.g. for           In a symmetric way, these CoCoS problems also arise for
leisure holidays and business trips as shown in Fig. 2 (bot-       items:
tom). These continuous cold-start problems are rarely ad-
dressed in the literature despite their relevance in industrial    Classical cold-start / sparsity: new or rare items
applications. Classical approaches to the cold-start problem
                                                                   Volatility: item properties or value changes over time
fail in the case of CoCoS, since they assume that users warm
up in a reasonable time and stay warm after that.                  Personas: item appeals to different types of users
   In the remainder of the paper, we will elaborate on how
CoCoS appears in e-commerce websites (Sec. 2), outline             Identity: failure to match data from the same item
some approaches to the CoCoS problem (Sec. 3), and end
with a discussion about possible future directions (Sec. 4).       New items appear frequently in e-commerce catalogues, as
                                                                   shown in Figure 3 for accommodations at Booking.com. Some
                                                                   items are interesting only to niche audiences, or sold only
2.    CONTINUOUS COLD-START                                        rarely, for example books or movies on specialized topics.
   Cold-start problems can in principle arise on both the user     Items can be volatile if their properties change over time,
side and the items side.                                           such as s phone that becomes outdated once a newer model
                                                                   is released, or a hotel that undergoes a renovation. In the
2.1    User Continuous Cold-Start                                  context of news or conversions, item volatility is also known
  We first focus on the user side of CoCoS, which can arise        as topic drift [9]. Figure 3 on the right shows fluctuations of
in the following cases:                                            the review score of a hotel at Booking.com. Some items have
                                                                   different ‘personas’ in that they target several user groups,
Classical cold-start / sparsity: new or rare users                 such as a hotel that caters to business as well as leisure
Volatility: user interest changes over time                        travellers. When several sellers can add items to an e-com-
                                                                   merce catalogue, or when several catalogues are combined,
Personas: user has different interests at different, possibly      correctly matching items can be problematic (identity prob-
    close-by points in time                                        lem).

Identity: failure to match data from the same user
                                                                   3.    ADDRESSING COLD-START
All cases arise commonly in e-commerce websites. New users            Many approaches have been proposed to deal with the
arrive frequently (classical cold-start), or may appear new        classical cold-start problem of new or rare users or items
when they don’t log in or use a different device (failed iden-     [11]. However, they mostly fail to address the more difficult
tity match). Some websites are prone to very low levels            CoCoS.
of user activity when items are purchased only rarely, such           The most popular strategy to address the classic cold-start
as travel, cars etc., leading to sparsity problems for recom-      problem is the hybrid approach where collaborative filtering
mender systems. Most users change their interests over time        and content-based models are combined, see [14] as an exam-
(volatility), e.g. movie preferences evolve, or travel needs       ple. If one of the two method fails due to a new user or item,
change. On even shorter timescales, users have different           the other method is used to ‘fill-in’. The most basic assump-
personas. Depending on their mood or their social context,         tion is that similar users will like similar items. Similarity
they might be interested in watching different movies. De-         of users is measured by their purchase history when warm,
pending on the weather or their travel purpose, they may           and by their user profile when cold. Conversely, similarities
want to book different types of trips, see Figure 2 for exam-      between items is computed by the set of users that pur-
ples from Booking.com.                                             chased them when warm, and by their content when cold.
   These issues arise for collaborative filtering as well as       In CoCoS, users change their interests, so both collabora-
content-based or hybrid approaches, since both user ratings        tive filtering and user-profile-based approaches can fail, since
or activities as well user profiles might be missing, become       looking at the past and similarities can be misleading. Items
outdated over time, or not be relevant to the current user         also suffer from volatility, although to a lesser degree, which
persona.                                                           makes the standard hybrid approach also problematic for
                      Activity Level
                                       1            51          101       151                             201         251        301          351
                      Activty Level                                                                Day


                                                                      Leisure                                                      Business
                                                                      booking                                                      booking

                                       1       11          21     31          41                     51          61         71    81      91
                                                                                             Day


Figure 2: Continuously cold users at Booking.com. Activity levels of two randomly chosen users of Book-
ing.com over time. The top user exhibits only rare activity throughout a year, and the bottom user has two
different personas, making a leisure and a business booking, without much activity inbetween.


410000                                                                                              9.1
390000      Available Properties                                                                      9
370000        at Booking.com                                                                        8.9
                                                                                Avg. User Rating




                   (2013)                                                                           8.8
350000
                                                                                                    8.7
330000
                                                                                                    8.6
310000
                                                                                                    8.5
290000
                                                                                                    8.4
270000                                                                                              8.3
250000                                                                                              8.2
      Jun       Jul             Ago           Sep        Oct    Nov     Dec                               1      51     101      151    201     251   301    351
                                           Month                                                                                  Day

Figure 3: Continuously cold items at Booking.com. Thousands of new accommodations are added to Book-
ing.com every month (left). The user ratings of a hotel can change continuously (right).


items. Hybrid approaches also ignore the issue of multiple                                               evaluation using real data.
personas.                                                                                                   Tavakol and Brefeld [20] propose a topic driven recom-
   Although, to our knowledge, the continuous cold-start                                                 mender system. At the user session level, the user intent
problem as defined in this work has not been directly ad-                                                is modeled as a topic distribution over all the possible item
dressed in the literature, several approaches are promising.                                             attributes. As the user interacts with the system, the user
   Tang et al. [19] propose a context-aware recommender                                                  intent is predicted and recommendations are computed using
system, implemented as a contextual multi-armed bandits                                                  the corresponding topic distribution. The topic prediction
problem. Although the authors report extensive offline eval-                                             is solved by factored Markov decision processes. Evaluation
uation (log based and simulation based) with acceptable                                                  on an e-commerce data set shows improvements when com-
CTR, no comparison is made from a cold-start problem                                                     pared to collaborative filtering methods in terms of average
standpoint.                                                                                              rank.
   Sun et al. [18] explicitly attack the user volatility prob-
lem. They propose a dynamic extension of matrix factoriza-                                               4.     DISCUSSION
tion where the user latent space is modeled by a state space
model fitted by a Kalman filter. Generative data present-                                                  In this manuscript, we have described how CoCoS, the
ing user preference transitions is used for evaluation. Im-                                              continuous cold-start problem, is a common issue for e-com-
provements of RMSE when compared to timeSVD [10] are                                                     merce applications. Industrial recommender systems do not
reported. Consistent results are reported in [5], after offline                                          only have to deal with ‘cold’ (new or rare) users and items,
                                                                                                         but also with known users or items that repeatedly ‘cool
down’. Reasons for the recurring cool-downs include the                 cess, Profile Management, and Context Awareness in
volatility in user interests or item values, different personas         Databases, 2013.
depending on user context or item target audience, or iden-         [5] F. C. T. Chua, R. J. Oentaryo, and E.-P. Lim. Modeling
tification problems due to logged-out users or items from               temporal adoptions using dynamic matrix factorization.
different catalogues. Despite the practical relevance of Co-            In IEEE 13th International Conference on Data Mining
                                                                        (ICDM), pages 91–100, 2013.
CoS, common literature approaches do not deal well with
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