Data and knowledge sources in recommender systems

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DATA AND KNOWLEDGE SOURCES IN RECOMMENDER SYSTEMS 
https://doi.org/10.5281/zenodo.6516440
 
Narziev Nosir 
TUIT, ATDT department, senior teacher 
Oblokulova Maftuna 
Student of TUIT 
Vafokulova Gavharbegim 
Student of TUIT 
 
Abstract: On the Internet, where the number of choices is overwhelming, it is 
necessary to filter, prioritize, and efficiently deliver relevant information in order to 
alleviate the problem of information overload, which has created a potential problem 
for many Internet users. Recommender systems solve this problem by searching a large 
amount of dynamically generated information to provide users with personalized 
content and services. This article explores the various features of recommender 
systems to serve as a compass for research and practice in the field of recommender 
systems. 
Key words: item, user, transaction, unary ratings, binary ratings, ordinal 
ratings, numerical ratings 
 
Recommender Systems are software tools and techniques providing suggestions 
for items to be of use to a user. The suggestions relate to various decision-making 
processes, such as what items to buy, what music to listen to, or what online news to 
read. 
“Item” is the general term used to denote what the system recommends to users. 
A recommender system normally focuses on a specific type of item (e.g., CDs, or 
news) and accordingly its design, its graphical user interface, and the core 
recommendation technique used to generate the recommendations are all customized 
to provide useful and effective suggestions for that specific type of item. 
Recommender systems are primarily directed towards individuals who lack 
sufficient personal experience or competence to evaluate the potentially overwhelming 
number of alter native items that a Web site, for example, may offer. A case in point is 
a book recommender system that assists users to select a book to read. In the popular 
Web site, Amazon.com, the site employs a recommender system to personalize the 
online store for each customer. Since recommendations are usually personalized, 
different users or user groups receive diverse suggestions. In addition there are also 
non-personalized recommendations. These are much simpler to generate and are 
normally featured in magazines or newspapers. Typical examples include the top ten 
selections of books, CDs etc. While they may be useful and effective in certain 

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situations, these types of non-personalized recommendations are not typically 
addressed by recommender system research. 
Recommender Systems are information processing systems that actively gather 
various kinds of data in order to build their recommendations. Data is primarily about 
the items to suggest and the users who will receive these recommendations. But, since 
the data and knowledge sources available for recommender systems can be very 
diverse, ultimately, whether they can be exploited or not depends on the 
recommendation technique. 
In general, there are recommendation techniques that are knowledge poor, i.e., 
they use very simple and basic data, such as user ratings/evaluations for items. Other 
techniques are much more knowledge dependent, e.g., using ontological descriptions 
of the users or the items, or constraints, or social relations and activities of the users. 
In any case, as a general classification, data used by recommender systems refers to 
three kinds of objects: items, users, and transactions, i.e., relations between users and 
items. 
Items. Items are the objects that are recommended. Items may be characterized 
by their complexity and their value or utility. The value of an item may be positive if 
the item is useful for the user, or negative if the item is not appropriate and the user 
made a wrong decision when selecting it. We note that when a user is acquiring an 
item she will always incur in a cost, which includes the cognitive cost of searching for 
the item and the real monetary cost eventually paid for the item. 
For instance, the designer of a news recommender system must take into account 
the complexity of a news item, i.e., its structure, the textual representation, and the 
time-dependent importance of any news item. But, at the same time, the recommender 
system designer must understand that even if the user is not paying for reading news, 
there is always a cognitive cost associated to searching and reading news items. If a 
selected item is relevant for the user this cost is dominated by the benefit of having 
acquired a useful information, whereas if the item is not relevant the net value of that 
item for the user, and its recommendation, is negative. In other domains, e.g., cars, or 
financial investments, the true monetary cost of the items becomes an important 
element to consider when selecting the most appropriate recommendation approach. 
Items with low complexity and value are: news, Web pages, books, CDs, movies. 
Items with larger complexity and value are: digital cameras, mobile phones, PCs, etc. 
The most complex items that have been considered are insurance policies, financial 
investments, travels, jobs. 
Recommender systems, according to their core technology, can use a range of 
properties and features of the items. For example in a movie recommender system, the 
genre (such as comedy, thriller, etc.), as well as the director, and actors can be used to 
describe a movie and to learn how the utility of an item depends on its features. Items 
can be represented using various information and representation approaches, e.g., in a 
minimalist way as a single id code, or in a richer form, as a set of attributes, but even 
as a concept in an ontological representation of the domain. 

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Users. Users of a recommender system, as mentioned above, may have very 
diverse goals and characteristics. In order to personalize the recommendations and the 
human-computer interaction, recommender systems exploit a range of information 
about the users. This information can be structured in various ways and again the 
selection of what information to model depends on the recommendation technique. 
For instance, in collaborative filtering, users are modeled as a simple list 
containing the ratings provided by the user for some items. In a demographic 
recommender system, sociodemographic attributes such as age, gender, profession, 
and education, are used. User data is said to constitute the user model. The user model 
profiles the user, i.e., encodes her preferences and needs. Various user modeling 
approaches have been used and, in a certain sense, a recommender system can be 
viewed as a tool that generates recommendations by building and exploiting user 
models. Since no personalization is possible without a convenient user model, unless 
the recommendation is non-personalized, as in the top-10 selection, the user model will 
always play a central role. For instance, considering again a collaborative filtering 
approach, the user is either profiled directly by its ratings to items or, using these 
ratings, the system derives a vector of factor values, where users differ in how each 
factor weights in their model. 
Users can also be described by their behavior pattern data, for example, site 
browsing patterns (in a Web-based recommender system), or travel search patterns (in 
a travel recommender system). Moreover, user data may include relations between 
users such as the trust level of these relations between users. A RS might utilize this 
information to recommend items to users that were preferred by similar or trusted 
users. 
Transactions. We generically refer to a transaction as a recorded interaction 
between a user and the recommender system. Transactions are log-like data that store 
important information generated during the human-computer interaction and which are 
useful for the recommendation generation algorithm that the system is using. For 
instance, a transaction log may contain a reference to the item selected by the user and 
a description of the context (e.g., the user goal/query) for that particular 
recommendation. If available, that transaction may also include an explicit feedback 
the user has provided, such as the rating for the selected item. 
In fact, ratings are the most popular form of transaction data that a recommender 
system collects. These ratings may be collected explicitly or implicitly. In the explicit 
collection of ratings, the user is asked to provide her opinion about an item on a rating 
scale. According to, ratings can take on a variety of forms: 
•
Numerical ratings such as the 1-5 stars provided in the book recommender 
associated with Amazon.com. 
•
Ordinal ratings, such as “strongly agree, agree, neutral, disagree, strongly 
disagree” where the user is asked to select the term that best indicates her opinion 
regarding an item (usually via questionnaire). 

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•
Binary ratings that model choices in which the user is simply asked to decide if 
a certain item is good or bad. 
•
Unary ratings can indicate that a user has observed or purchased an item, or 
otherwise rated the item positively. In such cases, the absence of a rating indicates that 
we have no information relating the user to the item (perhaps she purchased the item 
somewhere else). 
Another form of user evaluation consists of tags associated by the user with the 
items the system presents. For instance, in Movielens recommender system 
(http://movielens.umn.edu) tags represent how MovieLens users feel about a movie, 
e.g.: “too long”, or “acting”. 
In transactions collecting implicit ratings, the system aims to infer the users 
opinion based on the user’s actions. For example, if a user enters the keyword “Yoga” 
at Amazon.com she will be provided with a long list of books. In return, the user may 
click on a certain book on the list in order to receive additional information. At this 
point, the system may infer that the user is somewhat interested in that book. 
In conversational systems, i.e., systems that support an interactive process, the 
transaction model is more refined. In these systems user requests alternate with system 
actions. That is, the user may request a recommendation and the system may produce 
a suggestion list. But it can also request additional user preferences to provide the user 
with better results. Here, in the transaction model, the system collects the various 
requests-responses, and may eventually learn to modify its interaction strategy by 
observing the outcome of the recommendation process.