Recommender systems attempt to match users' preferences with items. To achieve this, they typically store and process a large amount of user profiles, item attributes, as well as an ever-increasing volume of user-generated feedback about those items. By mining user-generated data, such as reviews, a complex network consisting of users, items, and item properties can be created. Exploiting this network could allow a recommender system to identify, with greater accuracy, items that users are likely to find attractive based on the attributes mentioned in their past reviews as well as in those left by similar users. At the same time, allowing users to visualize and explore the network could lead to novel ways of interacting with recommender systems and might play a role in increasing the trustworthiness of recommendations. We report on a conceptual model for a multimode network for hotel recommendations and discuss potential interactive mechanisms that might be employed for visualizing it.
INTRODUCTION
Recommender systems (RS) typically build and maintain
network representations of the data they store about their
users and product catalogs. One of the earliest and most
wellknown examples is the “item-to-item” model introduced by
Amazon in its RS, which enabled the company to show,
using collaborative filtering techniques, what other products
were purchased by users who bought a certain product [
Linking user preferences with items and subsequently modeling the various relationships that arise between them can increase the accuracy of recommendations. Most commonly, RS employ 1-mode networks, meaning that all nodes are of the same type (e.g., users). Less frequent is the use of 2-mode networks [2], in which relationships are shaped between two different types of nodes, e.g., users and items. A relatively unexplored area of research concerns the usage of multimode (or n-mode) networks, in which vertices can be of three or even more types. To offer an example from the tourism domain, consider a hotel recommender that models the relationships that appear between users, hotels, and hotel amenities. Furthermore, the internal representation of such networks in usually not visible to users. This can happen for various reasons, ranging from privacy concerns to the inherent difficulty of creating a meaningful illustration of the structure of complex networks.
The purpose of this short paper is to advance the state of the art in two ways. First, we propose a concept of a multimode network for representing users, hotels, and hotel properties.
We argue that a hotel recommender can exploit multimode networks to generate more suitable suggestions. Second, we examine potential interactive mechanisms that could be developed to facilitate the presentation of the network to users. We believe that having access to additional means of visualizing hotel information would allow users to interact in novel ways with the RS. Furthermore, such novel interaction techniques could play a role in increasing the trustworthiness of recommendations.
In the following sections, we review related work on the usage of multimode networks in RS. We also investigate some of the typical visualization techniques that have been developed so far. Next, we present our conceptual model for a user-item-attribute network using references and examples from the hotel booking domain. Finally, we discuss how allowing users to interact with such a network might introduce novel ways of interacting with RS. Additionally, we open the discussion on whether exploiting this network could play a role in increasing the trustworthiness of recommendations.
RELATED WORK
The emergence of web communities and the sustained
growth of the user-generated content available online
provides important opportunities for RS. Incorporating
social network information has the potential to alleviate
cold-start and sparsity problems, which are inherent in
typical collaborative filtering approaches [
Figure 1 – Example of how a simple multimode network (right) containing three types of vertices—users (circle), topics (hexagon),
and hotels (rectangle)—could be generated from crawled user-generate content (left). In our conceptual model, users and hotels
are connected through topics, instead of directly.
methods in both fields as well as existing challenges are
highlighted, for example, in [
Exploiting 2-mode networks for improving the quality of
recommendations has already been achieved in various
domains. One such approach was used to derive similarity
information between artists and songs by exploiting data
collected from a music file-sharing network [
The problem of visualizing complex networks employed in
RS is also receiving increasing attention. Previous work has
been done, for instance, on facilitating the exploration of
recommended items by combining different entities (users,
tags, and agents) [
CO-STAYING NETWORK Based on our review of the literature, there appears to be a research gap with respect to the usage of multimode networks for RS. At the same time, the goal of providing interactive mechanisms for allowing users to explore the underlying graph has been studied less frequently.
Example Domain and Dataset Hotel booking is a domain in which the trustworthiness of recommendations is especially important. More generally, while selecting the domain we considered three aspects: 1) The choice should carry a substantial amount of risk for the user; 2) the items should have a reasonable set of attributes that need to be considered; and 3) there should be a large body of user-generated content available, in the form of reviews, photos, tags, and ratings, that can be leveraged in the presentation. Because of the first criterion, we decided against using the more common domain of movie recommendations. Hotel booking, on the other hand, fulfils all three conditions.
We crawled metadata and overall 838,780 user reviews for 11,544 hotels located in five major European cities from Booking.com1. This real-world dataset ensures access to a representative and interesting subset of hotels, covering as many types of amenities as possible. Furthermore, it features a diverse set of reviews contributed by various types of travelers—and who are travelling for different purposes— thereby maximizing the variety of user opinions and arguments. A characteristic of Booking.com is that all reviews on its site are verified, meaning they are written by people who have stayed in those hotels. This feature reduces the number of fake reviews.
Conceptual Model Our conceptual model, to which we will refer in the following sections as a “co-staying network”, has three types of vertices: users, topics (i.e. hotel attributes), and hotels. In contrast to a typical 2-mode network, users are not linked directly to the hotels in which they stayed. Instead, an edge is first created between the user and a topic. A second edge then links the topic to the hotel for which the review was submitted (Figure 1). This process is repeated iteratively for each topic in a review as well as for all user-contributed reviews. If a review contains references to more than one topic (as is often the case in hotel reviews), there will be several paths between its author and the hotel, each passing through a topic. Furthermore, if a user mentioned the same topic in more than one review, a separate pair of edges will be created for each instance.
The crawled user reviews in the dataset are mined to extract
topics. First, we identify attribute-value pairs such as
“comfortable bed” in the reviews. Then, these pairs are
merged with others that have the same meaning, e.g., “comfy
bed”. Next, using sentiment analysis, pairs are classified as
positive or negative hotel properties. Finally, pairs that
describe properties related to, for example, a hotel room, are
classified and clustered together. Broadly, topics are divided
into three main categories: room attributes (e.g., bed,
shower, minibar), hotel attributes (e.g., location, swimming
pool, parking), and hotel services (e.g., breakfast, Wi-Fi
quality, friendliness of staff). The complete procedure is
described in detail in [
The user’s sentiment regarding each topic is classified with
respect to its polarity (i.e. positive, neutral, or negative) and
strength. These two attributes are normalized and encoded
in the edges as a single value in the interval [
Similarly, higher positive values are associated with topics that a user has praised in a review (e.g., “wonderful location”).
If a hotel is part of a chain, our model also allows the creation of additional “soft links” from a topic to the rest of the hotels in that chain. This is based on the premise that these hotels share many characteristics—though not all. Thus, it is likely (and typically advertised as such by hotel brands) that travelers would have comparable experiences and access to similar amenities in every location that is part of a franchise.
This approach lets the RS exploit user opinions about selected topics when suggesting recommendations that might otherwise not have enough reviews. Among the topics that could be shared in this way are those concerning room layout and furnishings, breakfast, and hotel facilities. Topics such as those related to the hotel’s location or the service quality, on the other hand, would not be shared between hotels (Figure 2).
Clusters can be identified for each type of vertex in the costaying network. Topics can be characterized by their overarching category (i.e. hotel, room, or service).
Furthermore, by analyzing travel and review patterns,
additional relationships between hotel topics could be
identified. Hotels can be clustered based on whether they are
part of a chain as well as by looking at the most common
topics that characterize them. Finally, user similarity
measures can be extended to include, in addition to
demographic data (e.g., age range, country of origin) and
rating behavior, also experience (e.g., based on the number
of contributed reviews, frequency of travel, and the types of
hotels visited). Preliminary work on these aspects has
already been reported in [
The proposed model aims to improve upon traditional approaches to hotel recommending by creating stronger links between users, hotels, and hotel attributes. This would allow users to explore, in addition to the details of the recommended items, the public profiles and preferences of those guests whose reviews were exploited for generating the recommendations. Various forms of interactive mechanisms could be developed to facilitate this kind of exploration.
Interactive Mechanisms The initial focal point of the network would be the hotel that is being recommended. Users might ask themselves, “What do people talk about when reviewing this hotel?” The most common topics (ideally tailored to fit the user’s interests) could be shown radiating from the hotel. These should be clustered by category. Edges would connect these topics to the most representative users. Ensuring that the co-staying network remains accessible to users of the RS is a non-trivial task. One mechanism could involve providing sufficient criteria to filter topics and users to avoid information overload. Furthermore, the proportion of the co-staying network that is visible to the user should also be controlled, for example by implementing a “zoom in / zoom out” design pattern.
Users should also be able to refocus the network based on their interests or goals. The system should allow seamless transition between vertices, for example between hotels, from a hotel to a topic, or from a topic to the users who referenced it in their reviews. Interacting with a topic should bring up the review snippets in which it is mentioned. The user could then expand reviews that look promising to read them fully; alternatively, less interesting snippets could be hidden completely.
Trust in online reviews has been shown to depend on the
credibility of the source [
DISCUSSION AND FUTURE WORK
Including hotel topics as an additional type of vertex in the
network is expected to allow the RS to identify, with greater
accuracy, which items users are likely to find attractive
based on the attributes mentioned in their reviews as well as
in reviews left by similar users [
We believe that our co-staying network concept has the potential to increase the transparency of the RS by providing visual clues as to why certain recommendations are presented. Moreover, the proposed interactive mechanisms are meant to increase user control over the presentation of the recommended items. Thus, the trustworthiness of the recommendation should increase [6]. Developing evaluation criteria for measuring the effects of these novel interactions on the trustworthiness of the recommendations is planned for future work.
Our approach should also alleviate, to some extent, the data sparsity problem by sharing topics—and, therefore, user opinions—about hotels that are part of a chain. However, this will continue to remain an issue in the case of isolated hotel vertices, for which not enough user-generated information is available from the network. This aspect will be investigated in future work.
ACKNOWLEDGMENTS This work is supported by the German Research Foundation (DFG) under grant No. GRK 2167, Research Training Group "User-Centred Social Media".