"推荐系统手册第三版：最权威的指南与最新研究"

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The third edition of the "Recommender Systems Handbook" is a comprehensive guide on the cutting-edge technology of recommender systems. Edited by Francesco Ricci, Lior Rokach, and Bracha Shapira, this handbook covers a wide range of topics related to recommendation algorithms, systems, evaluation, and applications. The editors, who are experts in the field of computer science and information systems engineering, have compiled contributions from leading researchers and practitioners to provide insights into the latest advancements in recommender systems. With a focus on both the theoretical and practical aspects of recommender systems, this handbook offers a thorough overview of the different types of recommendation techniques, including collaborative filtering, content-based filtering, and hybrid approaches. Furthermore, it explores various evaluation metrics and methodologies for measuring the performance of recommendation algorithms, as well as real-world applications of recommender systems in e-commerce, social networks, and personalized services. Overall, the "Recommender Systems Handbook, 3rd Edition" serves as an essential reference for researchers, students, and industry professionals interested in understanding and implementing state-of-the-art recommender systems technology.

6 F. Ricci et al.

• Increase the user satisfaction. A well designed RS can also improve the

experience of the user with the site or the application. The user will ﬁnd the

recommendations interesting, relevant and, with a properly designed human-

computer interaction, he or she will also enjoy using the system. The combination

of effective, accurate recommendations and a usable interface will increase the

user’s subjective evaluation of the system. This, in turn, will increase system

usage and the likelihood that the recommendations will be accepted.

• Increase user ﬁdelity. A user should be loyal to a website which, when visited,

recognizes the old customer and treats her as a valued visitor. This is a standard

feature of an RS since many RSs compute recommendations, thus leveraging

the information acquired from the user during previous interactions such as the

user’s ratings of items or user’s visited pages. Consequently, the longer the user

interacts with the site, the more reﬁned the user’s model becomes: the system’s

representation of the user’s preferences develops and the effectiveness of the RS

output to customize and match to the user’s preferences is increased.

• Better understanding of what the user wants. Another important function of an

RS, which can also be leveraged by other applications of the RS owner, is the

description of the user’s preferences, which are collected either explicitly or

predicted by the system. The system owner, but also the item providers, may

decide to reuse this knowledge for a number of other goals, such as improving the

management of the item’s stock or production. For instance, in the travel domain,

destination management organizations can decide to advertise a speciﬁc region

to new customer sectors or advertise a particular type of promotional message

derived by analyzing the data collected by the RS (transactions of the users).

We mentioned above some important motivations as to why the system owner

introduces an RS and the item providers offer their services/items through the RS

platform. But naturally users will use an RS if it will effectively support their tasks

or goals. Consequently an RS must balance the needs of these three stakeholder and

offer a service that is valuable to all of them. Hence advanced evaluation techniques

must be introduced in order to proper assess the value and the impact of an RS across

multiple groups of stakeholders, as it is discussed in Chapters “Multistakeholder

Recommender Systems” and “Value and Impact of Recommender Systems”.

Without fully describing the range of dimensions that drive the adoption of RSs,

which is discussed in Chapter “Value and Impact of Recommender Systems”, we

want now to focus on the end user side and refer to Herlocker et al. [36], a paper

that has become a classical reference in this ﬁeld. The authors deﬁne eleven popular

user’s tasks that an RS can assist in implementing. Some may be considered as

the main or core tasks that are normally associated with an RS, such as offering

suggestions for items that may be useful to a user. Others might be considered

as more “opportunistic” ways to exploit an RS. As a matter of fact, this task

differentiation is similar to what happens with other digital “tools”, e.g., a search

engine. Search engines primary function is to locate documents that are relevant

to the user’s information need, but it can also be used to check the importance of

Recommender Systems: Techniques, Applications, and Challenges 7

a webpage (looking at the position of the page in the result list of a query) or to

discover the various usages of a word in a collection of documents.

• Find Some Good Items: Recommend to a user some items as a ranked list along

with predictions of how much the user would like them (e.g., on a scale of

one-to-ﬁve stars). This is the main recommendation task that many commercial

systems address (see, for instance, Chapters “Music Recommendation Systems:

Techniques, Use Cases, and Challenges” and “Multimedia Recommender Sys-

tems: Algorithms and Challenges”). We note that some systems do not show the

predicted rating, especially when users may not be interested in understanding

why the item is recommended (e.g., in music).

• Find all good items: Recommend all the items that can satisfy the user needs, in

such cases it is insufﬁcient to just ﬁnd some of them. This is especially true when

the number of items is relatively small or when the RS is mission-critical, such as

in medical or ﬁnancial applications. In these situations, in addition to the beneﬁt

derived from carefully examining all the possibilities, the user may also beneﬁt

from a motivated ranking of these items or from additional explanations that the

RS generates (see Chapter “Beyond Explaining Single Item Recommendations”).

• Annotation in context: Given an existing context, e.g., a list of items, emphasize

some of them depending on the user’s long-term preferences. For example, a TV

recommender system might annotate which TV shows displayed in the electronic

program guide (EPG) are worth watching (Chapter “Social Recommender

Systems”, provides interesting examples of this task).

• Recommend a sequence: Instead of focusing on the generation of a single

recommendation, the idea is to recommend a sequence of items, one by one or as

a whole. Typical examples include recommending, a next point of interest to visit,

a TV series, a book on RSs after having recommended a book on data mining, or

a compilation of musical tracks (see Chapters “Music Recommendation Systems:

Techniques, Use Cases, and Challenges”, and “Session-Based Recommender

Systems”, for more examples and discussion).

• Recommend a bundle: Suggest a group of items that ﬁts well together. For

instance, a travel plan may be composed of various attractions, destinations, and

accommodation services that are located in a delimited area. From the point of

view of the user, these various alternatives can be considered and selected as a

single travel destination [45].

• Just browsing: In this task, the user browses the catalog without any imminent

intention of purchasing an item. The task of the RS is to help the user to browse

the items that are more likely to fall within the scope of the user’s interests for

that speciﬁc browsing session. This is a task that has also been supported by

adaptive hypermedia techniques [20].

• Find credible recommender: Some users do not trust recommender systems, thus

they play with them to see how good they are at making recommendations.

Hence, a certain system may also offer speciﬁc functions to let the users test

its behavior in addition to those just required for obtaining recommendations.

8 F. Ricci et al.

• Improve the proﬁle: This relates to the capability of the user to provide (input)

information to the recommender system about what he or she likes and dislikes.

This is a fundamental task that is strictly necessary to provide personalized

recommendations. If the system has no speciﬁc knowledge about the active user,

then it can only provide the same recommendations that would be delivered to an

“average” user (see Chapter “People-to-People Reciprocal Recommenders” for

an application domain where preference elicitation is critical).

• Express s elf: Some users may not care about the recommendations at all. Rather,

what is important to them is that they be allowed to contribute with their ratings

and express their opinions and beliefs. The user satisfaction for that activity can

still act as leverage, resulting in the user’s continued loyalty to the application (as

we mentioned prior, in discussing the service provider’s motivations).

• Help others: Some users are happy to contribute with information, e.g., their

evaluation of items (ratings), because they believe that the community beneﬁts

from their contribution. This could be a major motivation for entering informa-

tion into a recommender system that is not used routinely. For instance, with an

automobile RS, a user who has already purchased a new car is aware that the

rating entered in the system is more likely to be useful to other users rather than

to oneself, the next time a new-car-purchase is contemplated.

• Inﬂuence others: In Web-based RSs, there are users whose main goal is to

explicitly inﬂuence other users into purchasing particular products. As a matter

of fact, there are also malicious users that may use the system simply to promote

or penalize certain items.

As these various points indicate, the role of an RS within an information system

can be quite diverse. This diversity calls for the exploitation of a range of different

knowledge sources and techniques. In the next two sections, we discuss the data that

an RS manages and the core technique used to identify the right recommendations.

3 Data and Knowledge Sources

RSs are information processing systems that actively gather various kinds of data

in order to build their recommendations. The data used within an RS can relate

to three kinds of objects: items, users, and interactions between the users and

the items. Some systems collect users’ features, such as their areas of interest,

age, or gender, while other gather items’ characteristics, such as the genre of a

movie or the price of an item. All systems, in order to learn users’ preferences

and generate recommendations, gather users’ interactions with items. In practice,

the data and knowledge sources available for an RS can be very diverse, and their

effective exploitation depends on the recommendation technique and the available

computational resources (see Sect. 4).

Items Items are the objects that are recommended. Available items data depends

on the complexity of acquiring them, for instance item features, which also

Recommender Systems: Techniques, Applications, and Challenges 9

depends on the availability of meta-data. For example, in a multimedia RS,

items such as images, require special image analysis algorithms to extract their

features from raw content. Text-based items, such as news, or items that have

long descriptions, s.a., reviews, require special algorithms to analyze the text and

extract meaningful relevant features from the text. Chapter “Natural Language

Processing for Recommender Systems” describes NLP methods that can be used

to extract meaningful information from textual descriptions of items (and users).

For example, by using such methods, features can be extracted from experts reviews

[55], and they may alleviate the cold-start scenario, i.e., when dealing with items that

have not gained yet enough interactions. Chapter “Semantics and Content-Based

Recommendations” presents methods for the representation of items (and users)

adopting semantic-aware representations of textual content. While early models

were not able to extract from the textual content a complete description of the item,

nor to encode semantic relationships between terms, semantic aware systems enable

to give meaning to information expressed in natural language and obtain a deeper

comprehension of the information conveyed by textual content, hence leading to

better RSs. Chapter “Semantics and Content-Based Recommendations” reviews

the main two approaches for semantic representation: endogenous, i.e., relying

on the distribution of keywords in documents, and exogenous, which resolves

semantic synonymy, polysemy and other language challenges, by utilizing external

knowledge, like taxonomies ontologies or other language related information.

Users Users of an RS, as we mentioned above, may have very diverse goals

and characteristics. In order to personalize the recommendations and the human-

computer interaction, RSs exploit a range of information about the users. This

information can be structured in various ways. User data is said to constitute the

user model [16, 30]. 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,

an RS can be viewed as a tool that generates recommendations by building and

exploiting user models [13, 14]. The selection of what information to model depends

on the recommendation technique, the availability of data and the efforts required

to gather and extract the knowledge.

For instance, in collaborative ﬁltering, a user is modeled as a simple list of his

ratings for certain items. In context aware RS (see Chapter “Context-Aware Rec-

ommender Systems: From Foundations to Recent Developments”) the user model

incorporates the contextual information to be utilized during the recommendation

process in order to recommend items to users under speciﬁc contextual situations.

For example, by using the temporal context, a travel recommender system would

include contextual features describing the weather, and the time of year when

the vacation was consumed, so that a vacation recommendation in winter can

be very different from the one recommended in summer. In content-based RSs

(see Chapter “Semantics and Content-Based Recommendations”) users could be

modeled by the content features of the items they consumed, and, similarly to the

items, such features may derive from of a variety of tabular and unstructured content.

10 F. Ricci et al.

For example, textual user generated reviews about consumed items may be utilized

to enrich the user model [22, 55].

Interactions RSs record the interactions between their users and items in log-

like data that stores important information generated during the interactions. This

information is useful for the recommendation generation algorithm that the system

is using. For instance, a log may contain a reference to the item purchased by the

user, the events that led to the buying event (e.g., browsing, clicking, inclusion in the

basket, etc.), the rating that the user assigned to the item, or any other information

that the system collects about the time, the location of the interaction. An important

part of the information about the interactions is the user feedback to the item. User

feedback can be explicitly provided by users or implicitly inferred from the users’

behaviour.

Explicit feedback is known to be more reliable than implicit one and provides

a level of preference of the user for an item (e.g., ratings 1–5). However, explicit

feedback is often not available, or very sparse, since many users would not bother

to provide it. Ratings are the most popular form of explicit feedback data that RSs

collect [59]. According to [63], ratings can take on a variety of forms:

• Numerical ratings such as the 1–5 stars used in the RS offered by 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 his or

her opinion regarding an item (usually via questionnaire).

• Binary ratings that model choices in which the user is simply asked to decide if

a certain item is good or bad.

Implicit feedback relates to the inference of the user’s preference for an item

from the observed interaction of the user with the item, for instance during a

recommendation session (see Chapter “Session-Based Recommender Systems”).

Implicit feedback is typically available for many more items than those available

for explicit feedback. In fact, various types of user-item interactions (clicking,

buying, adding to the basket, and many more) provide some implicit indication of

the user’s preference. Such feedback can be considered as unary ratings, and does

not indicate a speciﬁc level of preference. The interactions are typically interpreted

as positive implicit preferences. However, the absence of an interaction cannot be

simply considered as a sort of negative feedback. Usually, there is not enough data

to precisely derive the reasons for a missing interaction: the user may dislike the

item, or may have not seen it at all, or may have consumed it already in the past.

Chapter “Item Recommendation from Implicit Feedback” describes the challenges

of building RSs from implicit feedback, and provides methods to address them.

First, since implicit feedback refers to items that the users consumed in the past,

the data is noisy and gives a weak signal of positive preferences. Most of the items

do not have interactions and considering all these absence of interactions as a weak

negative signals of preference leads to high training costs. That makes standard

training algorithms hard to apply.

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