The authors present a possible approach for a new general purpose recommender architecture, one which complements the current proven and tested techniques (User Model, Collaborative Filtering, Content Based Filtering), used in some everyday business scenarios, balancing with newly developed personalization procedures and methodologies. The overall objective is to try to tackle some of the typical shortcomings of traditional recommender systems (Cold start, dilution of the “personal color” in a sea of collective thinking…), by effectively balancing the amount of collective intelligence used against a more “personal affinity” score. This, the authors call PPM (Product Profile Matching), an approach which ignores collective results and relies mainly on the intrinsic affinity between the nature of both the subject and the item. Hence the use of the name “balanced”, because of the balance struck between A.I. techniques and Applied Personalization Techniques used to make a better recommendation. The authors also focus on the need for proper selffulfilling techniques in order to illustrate the paramount importance of improving and extending the control that existing recommender systems give users in order to optimize the user experience. An example based on the author's previous work in the field of TV content recommenders is presented to illustrate the validity of our approach.
Information overload has become a problem in recent times. Increasingly, system
users encounter difficulties in finding the information they need. Recommender
Systems [
Existing recommender systems have certain limitations, which although they do not hamper the overall usefulness of the system they prevent the “perfect recommendation” from being provided. The “perfect recommendation” is somewhat difficult to specify, but we define it as:
“The result of ascertaining the exact desires of the individual using a recommender system, taking into account not only the knowledge of the whole network, but the particularities of the user AND the items available, which are relevant to the recommendation process”.
Some of the difficulties of recommenders are well known and are usually dealt with
in different ways: “Cold start” is perhaps the best known one; clearly there is no real
way for a recommender to provide useful recommendations from the start without an
initial recommendation from other users. In Movielens different techniques are
developed to select some items (films), shown to the user in order to create an initial
model. One of the criteria to show initial items to the users is to rank items according
to their particular relevance to these individuals. A good overview of the ranking
algorithms is presented in [
Best match? What we found interesting is that while we aim to achieve perfection in terms of pattern recognition and other such mathematical delicacies, we ignore as “non manageable” the capability to effectively and precisely draw a unique, non clustered, image of our user. This is where user modeling comes to the rescue: the main idea behind user modeling is to produce a “model” that tries to identify the key attributes belonging to a specific domain (in the case of Pandora, musical tastes) which can truly identify the user.
The problem with user modeling is a simple one: The model is produced (as accurately as possible), but it does not provide a suitable technique to ensure that several objectives are achieved. These objectives, fundamental in the overall process to guarantee a perfect personal recommendation experience, are the following: a) To take the user from a “dummy” experience (i.e., one where they have had no involvement in the recommendation process) to being fully in control (fully tuning all the parameters included in the recommendation process) in a smooth and logical transition, b) To provide an effective way of interacting with the user in order to engage him/her to produce more and more explicit feedback and profile detail. c) To provide an effective framework for creating a constant “quid-pro-quo” scenario between provided data and improved responses from the recommender
User modeling provides a framework, but does not resolve the problem entirely.
The user is not naturally enticed to cooperate, because there is no real incentive. In
most approaches to recommendation engines there is one notable flaw: The
systematic relying on Machine Learning and non-explicit feedback from the user to
create the user model, where the possibility of truly engaging the user in the
construction of their own profile is practically nonexistent. Why does this happen?
Mainly for one reason; most current approaches to recommender systems come from
the “hard sciences” i.e., those related with knowledge based on rigid disciplines with
fixed definitions such as mathematics and engineering Most of the sciences
addressing the question of personalization however deal with “lighter” disciplines like
etiology, psychology, marketing and so on , i.e., disciplines with a somewhat laxer
approach to definitions and even contradictory solutions for identical problems.
Therefore it seems that there is no possible way to rationalize and approach these
disciplines systematically so it is best to rely on tried and tested scientific approaches.
Unfortunately, although some work is emerging in this area [
Given the fact that “personalization” is a fairly vague word, which encompasses a lot of different definitions and approaches, with varying degrees of depth and no common consensus on the definition, we provide a series of basic components for our framework, dealing with the Personalization aspects of our work. Our proposal is a Balanced Recommender System defined as follows: "A balanced recommender system is a approach which combines a recommender algorithm based on implicit, collective and behavioural data with a user’s, explicit, user-centric and specific user model. The system uses additional tools and techniques provided to manipulate, enrich and fine tune the final recommendation.” The specific user model is not a generic one but depends entirely on the type of recommender involved (tv recommender, book recommender etc). Also the overall degree of involvement of the user in the creation of his/her profile has a significant impact on the quality of the final recommendation.
In this work we illustrate how we concluded that there was a need for this new type of recommender and describe the logic used to build our system. 2.1
Supposedly, recommender systems - even the least sophisticated ones- deliver personalization. They deliver “personalized” recommendations, make “personalized” offers and deliver “personalized” messages. In our opinion however, this is not entirely the case. A detailed definition of personalization has been included in a previous reference1 but for the purposes of this paper we will try to provide a less complex explanation:
“Personalization is a process which basically tries to adapt as closely as possible a product/message to a customer/speaker. The more accurate the analysis, the more accurate will be the recommendations. If we manage to grab the interest of our user 1 One of the authors of this paper has published a book and several papers on a systematic approach for handling this problem, from which we have taken some definitions and some basic building blocks. Unfortunately, to date it is currently only available in Spanish: “Personalización” – Pearson Financial Times 2004 ISBN 9788420543543 and to obtain/understand their preferences, we will be more successful in our selling or communication proposition.“
Therefore, we need to ascertain user preferences on the aspects relevant to our proposal (i.e.,, if we are trying to sell a Chinese cookery book, it is irrelevant to know the customer’s hair color, but it is important to know that s/he is fond of cooking). Equally it is vitally important to know which communication channel the user is more receptive to. Adaptation involves a continuous process. Let us imagine for example that our objective is to paint a whole wall black. There is no such thing as “instantaneous wall painting” but rather it must be achieved one brush stroke at a time. In our case, the trivial data (i.e. name, address etc.,) are the equivalent of the brush strokes. When we use the word “personalization”, the problem is that the verb “personalize” is like a kind of light switch, either it is on or off. Either you personalize or you do not. What really happens however is that there is a continuous process involved: It may be not be possible to personalize, it may be possible to personalize a little, it may be possible to have more or less accurate personalization, or have a completely tailor-made personalization. Clearly, “real” personalization is the latter of these possibilities, those really relevant to the user. Besides increasing the potential success of every subsequent interaction with our customer, there is another positive collateral effect arising from the use of personalization: After several relevant communications have been made, the customer/user pays more and more attention to our messages, because he has perceived them as relevant, unlike most of the communications they receive, where he/she perceives him/herself as an anonymous receiver. This precision is quite important, as we feel that there are too many unfounded claims of “delivers personalized results”. In reality, the results may differ according to the true use of personalization in each scenario.
The recommender presented here is adapted to different systems and has been
adapted with new features such as the one presented for the first time in this article
(see sections 2.2, 2.3 and 2.4). Basically our proposal is a hybrid recommender that
combines different features and separates long term and short term assumptions of the
user model as presented in [
Summarizing, we have different recommenders: content based recommendations, a short term and a long term model, an item-item collaborative recommendation, one based on tags and the PPM. Each of these recommender approaches produces a list of programs and in order to calculate the relevance of each one for the user, we compute a weighted sum, where α+β+φ+δ+ω=1. This determines the importance that we give to any of the four recommenders mentioned above (short and long are based on the same content based recomender). See Eq. (1). These parameters have an initial value that is updated for each recommender according to the amount of data available for it (i.e. if the number of user tags grows, this recommender will be given more importance). Besides automatic adjustment, the user can also express his/her preferences using the self fulfilling technique explained in 2.4.
short long collab .
R(user , item ) ( Ru,i Ru,i ) Ru,i tags Ru,i RuP,PiM (1) 2.2
For the cold start scenario we propose different solutions. One of these is to propose items based on their relevance for the users. As users evaluate the items (i.e., the book, tv program, artist etc) the relevance must take into account the number of evaluations of this item(FOi), the quality of its evaluations(Av(Oi)), and the total number of evaluations input into the system(|evO|). It is important to make a good comb ination of these factora because if not we may find situations like the following in some systems:
At first glance, this approach may seem logical, as it means that an item will obtain its relevance according to the average of its evaluations. Let us suppose for the following examples that our items can be evaluated from (1 meaning very bad to 5 meaning very good). With eq. (2) there may be some strange results: if a document has been evaluated 100 times with an average of 4.2 it will be less relevant than one that has been evaluated only once with 5. This solution benefits newcomers and makes the top list very changeable and unstable.
On the other hand we could take equation (3). This would give the older items a
better position in the ranking because they have been evaluated many times, even if
the evaluations were not particularly good. So, how can we obtain the right solution?
We wish to give reflect an appropriate value for well evaluated newcomers but also
respect those items evaluated many times. If we analyze the information retrieval
experience, a similar problem arises when trying to rank documents according to a
query. The algorithm TF-IDF [
This equation, (4) works quite well but the logarithmic function gives much more
priority to the newcomers, and if an item has been evaluated many times
independently of its evaluations it becomes less relevant because the logarithm
approaches zero (these are extreme cases), also the difference between different
evaluations is not taken into account. Finally, inspired by the modification of IDF by
Joachim [
To clarify with a simplified example let us suppose there are 7 items that users can evaluate in the range 1-5. We have the average of their evaluations Av(Oi), the number of times the items have been evaluated FOi. and the total number of evaluations done in the system |evO|=Sum(FOi). In Table 1 we can find on the left how the algorithm (4) sorts the items and on the right how algorithm (5) does so. We can observe that the results on the left may not be entirely accurate because for example an item evaluated 100 times as 2 is ranked better than another that has been evaluated 10 times as 4. The square root of the equation (5) solves this problem and its ranking looks much more realistic. The equation (5) can be used with different goals: 1) To create top lists, i.e. for the top list of favorites (items are sorted because users have selected them as favorites) or the popular items . (sorts the items according to popular user evaluations). It could be used for example to obtain the most recent and popular selections 2) To tackle the Cold start problem. New users could obtain recommendations of the most popular items in the system as other personalized recommendations cannot be calculated yet or 3) To have initial estimations if the Personal and Explicit Profile (explained in the following section) has not yet been created.
Table 1. Comparison of the Ranking using a) the Logarithmic eq. (left) and b) the
Square eq.(right) Av(Oi) 5 4 5 2 4 5 2
FOi 40 40 20 10 3 10 100
Log(eq.(4)) 2,21336768 1,77069415 1,55311241 1,03307474 0,79981639 0,41624581 0,3999082 2.3
Av(Oi) 5 5 4 4 5 2 2
FOi 40 20 40 10 3 100 10
Square(eq.(4)) 10,5408205 7,45348565 6,74612512 3,37306256 2,88672258 2,66664009 0,84326564 We understand PPM as a continuous process that involves the following elements: A) A detailed User Explicit Profile (usually considered the user model), regarding the specific domain that in each system is being covered. B) A product item, (which we can associate to something called the item model).
This would involve the characteristics of the item relevant to the decision making process C) A complete detailed model of the application of both group A and B, which could predict individual affinity between the specific user profile and the item model, not on a cluster basis but on an individual basis.
It is a continuous process because all three models are subject to continuous improvement, and a possible initial approach could yield some information helpful for improving every model. The key here is relevance. The criteria of inclusion /exclusion of attributes in these models is not to do with how easily they can be obtained but their relevance in the Product-Profile relationship. The design of both the attributes and the relationship must be done independently of the feasibility or any other factors that could hamper the creation of the best possible affinity mechanism model. Compromises can be made later but the model should take into consideration every single cause-effect that could influence the affinity model.
PPM involves a dedicated effort to create a taxonomy that must be addressed in a professional way, by people with knowledge of both business fields (User model – Item Model). Let us imagine a PPM model created for an online bookstore: There should be a clear customer expert behind the creation of the customer model, a librarian perhaps, and some kind of product manager behind the creation of the product mode. The combination of these, perhaps someone from a commercial department, should be behind the affinity model.
Product characterization does not need to be extensive if the relevance prerequisite
previously mentioned is fulfilled – the authors have produced a paper on a process of
PPM from the Product side [
How does PPM relate to balanced recommenders? A Proper PPM schema should be included in balanced recommenders for the following reasons: - It must deal with the “individual” aspects of the recommendation, like the rest of the aforementioned techniques discussed previously. - It provides a strong initial starting point thereby avoiding the Cold Start scenario (working in conjunction with the aforementioned solutions), translating the responsibility of preventing the cold start to the user providing detailed info on his model (as the product model has been previously covered, as well as the affinity application model).
- It offers a strong model to refine the overall recommender results. 2.4
Another key component of a Balanced Recommender system is the existence of Self fulfilling capabilities and a proper Self Fulfilling strategy must be in place. Let us try to develop this. Most recommenders have adopted an approach that we strongly discourage – that of keeping the user away from the underlying algorithm used. This is like telling users “Trust us, we are really smart” – not the best approach for a supposedly “personalized” approach. Users do not have a real sense of being in control and we think that it is important to allow people to decide to what degree outside or collective intelligence should play a part in the suggestion / decision provided by the recommender. As our current proposal involves several different features and it is highly likely that different users will have different degrees of collaboration, the following steps should be taken: - A step-by-step system should be developed to educate the customer on how to move from a fully automated to a fully (user) controlled contribution for every factor - The degree of precision in the recommendation should be directly linked to the following factors: o How well the user understands the underlying model and how this affects their input and fine tuning, o The degree of fulfillment of the data user model proposed, through a clear “tit for tat” proposition – you provide me with better data and a better recommendation will be the result. o The degree of precision must not be related to external factors such as intrinsic data quality or the degree of training of the recommender network - All the contributions involved in the recommendation algorithm should be shown for the customer to fine tune and adjust their preferences once they have received appropriate training on the matter: i.e., they should be informed of how much weight was given to content based evaluations, how much to collaborative, how much to PPM etc…
With some kind of visual metaphor and some easy feedback procedures we are sure that people will have a much better experience with recommenders than has been the case up to now (see Fig. 2).
Mirotele2 is a joint venture between the authors in which we have been involved
[
relevant attributes involved in TV preferences), a whole self fulfilling environment
(where advanced users can start to tinker with the data involved in the algorithm once
they begin to appreciate how it works). We have developed a whole PPM schema
combining the aforementioned user model with a fingerprinting method for TV
Programs [
- Although our Balanced Recommenders incorporate a hybrid approach, they are not the same as hybrid recommenders. This is not merely a question of semantics. We are mixing personalization techniques and classic recommender techniques. In our recommenders, the knowledge domains are quite different and the result is much more intuitive than in the hybrid approach. Personalization is a science in itself and trivial approaches must be avoided. We have found several personalization techniques like PPM that are appropriate for expanding the current recommender schema. We do not discard the possibility of enriching our schema with other techniques in the future, and have found that “balancing” a purely scientific approach with personalization techniques has produced an extremely good/promising result. - In the future there will be a systematic shift from current recommender schemas to balanced approaches like the one we present here. - We foresee a new golden age in the use of recommenders systems as they gradually become important information-organizers, substituting those currently in existence (mostly Search engines).
We are currently working on the implementations of our schemas and algorithms and plan to continue researching the area of balanced recommenders, in particular dealing with the less documented and structured aspects of personalization techniques. At the same time we will continue to improve our tools and attempt to determine as much as possible the correct combination of every factor considered here in order to achieve “the perfect recommendation”. Perhaps it is as elusive as the perfect cocktail, but our ultimate goal is to improve current.