This section describes the proposed approach of a concept hierarchy to obtain user prefer- ences or information. We also present a method to evaluate the importance of concepts in a concept hierarchy. The main objective of this thesis is to enhance personalised recom- mendation approaches. Typically, most recommender systems make recommendations based on ratings that users have assigned to items. However, because the amount of user ratings data is insufficient to capture user preferences, other information about users and
items can be used to learn and obtain user preferences.
Inspired by the approach of Ziegler et al. [121] and Weng et al. [111], this thesis provides an effective way to summarise useful knowledge about existing items, which significantly contributes to the cold-start problem. To formalise the problems, this thesis focuses on using item taxonomic information as domain-specific knowledge. The struc- tural information of the taxonomy tree structure and the relationship between users and items according to the taxonomic categories utilised by users are taken into consideration when generating a new structure to determine user preferences (or needs).
This thesis proposes an effective novel framework for using the concept hierarchy modelto gauge the interest a user ua has in an item bk according to a set of taxonomic
concepts that the user interacts with and the item has. The rationale for this is that users may have interest in common concepts, even though they have not rated the same items. We provide a method to help systems perform the same role as human beings, in that it allows them to make assumptions and extrapolate a person’s interests and abilities from limited information. Some information is more useful than others. For example, from knowing that a person has shown an interest in a toddler’s book about counting, it would be a sound assumption that this person has children in his or her life and would therefore also be interested in a whole range of products and topics or categories related to children. Instead of representing user preferences using only ratings data, the system can learn these preferences (or interests) through the concept taxonomy of items in two directions of the hierarchy: vertical and horizontal. The basic idea of a concept hierarchy is that a user has preferences that should be associated with concepts in both dimensions of the hierarchy to reflect the fact that user preferences are broader than single items. The ver- tical direction describes concept relations as ‘is-a’ (general to more specific) relationship between concepts, and the horizontal direction describes a sequence of concepts. A user’s interest in the concepts on the horizontal span (on the same level) indicates his or her
preferred concepts and provides a list of priorities.
Figure 3.4: An example fragment of an item taxonomy extracted from item taxonomic descriptors
For example, in Figure 3.4, in the domain of books, we have a concept Comput- ers&Technologyand sub-concepts Web&Design, Programming, Microsoft, etc. in which Web&Design, Programming or Microsoft have an ‘is-a’ concept relationship with Com- puters&Technology. A user may like both Computers& Technology and Web&Design from a general interest to a more specific interest (vertical). As there are three sub- concepts, Web&Design, Microsoft and Programming, at the same horizontal concept level, the user may like both Web&Design and Programming, but might regard Web&Design as more important than Programming, based on their interest in the concept from a specific interest to a more general interest. In other words, the interests of the user in each concept are a user’s horizontal list of concept priorities. It is convenient for users to describe what they want in the concept hierarchy without any value or weight. Therefore, user preferences can be represented by the user concept hierarchy. Likewise, the relevance of the item to the taxonomic concepts can be represented by the item concept hierarchy.
This model provides a novel structure for new users to approximate their needs in a two-dimensional hierarchy. We can easily transfer a set of rated items into a concept hierarchy using item taxonomic information. As such, this model is also applicable to new users who have only rated a few items.
3.3.1 The Definition of Concept Hierarchy
To better understand how to construct the representation of the user and the item based on concept hierarchy, the following concepts must be included:
Figure 3.5: An example of item concepts taxonomy transferred into a concept hierarchy model
• Concept preferences: user interests or preferences for concepts or categories of items. Concept preferences record a user’s preferred concepts and can be obtained explicitly or implicitly. Explicit concept preferences can be represented as a set of keywords, categories, topics or concepts provided directly by the users. The users can explicitly declare which topics they are interested in, such as through search queries or concept/category interests defined in his or her user profile. Implicit concept preferences can be represented by a set of keywords that are extracted from
the content or taxonomic concepts of the items that the user clicks, buys, browses, rates, or tags. The implicit concept preferences are generated automatically based on user behaviours.
• The concept hierarchy H = {L1, L2, . . . , Lp} is defined as a set of concept levels
where each level Li ∈ H includes a sequence of concepts. Level Lican be assigned
to each node in the concept taxonomy. The first level starts from the root node. The level number of root node is defined as 1, and the number of the other levels increases towards the leaf nodes by one plus the level of its parent. The user preferences in the concept taxonomy ci can be described in two directions of the
hierarchy H.
In the vertical hierarchy, because the relationship between concepts is an ‘is-a’ relationship, L1 is the top level, or parent node, that describes the most general
concepts and Lp is the bottom level that describes the most specific concepts. We
can indicate that the interest of the user ua to the concept taxonomy ci in the top
level of the hierarchy is more general, and it becomes more specific towards the lower levels or leaf nodes. Given that concept cy > cx or cx is-a cy, that means
cy appears in one of the upper levels and cx appears in one of the lower levels, or
the leaves. Horizontally, the interest of the user in each concept can be indicate by his or her list of concept priorities. On the same level of the hierarchy, the order of the concepts on the list is stored and sorted from left to right according to their importance. When two concepts ca, cb ∈ C are on the same level, a user may be
more interested in cb than ca.
Figure 3.5 gives an example of how to generate a concept hierarchy from item taxonomic descriptors. Given an item taxonomic descriptor dx = c1 > c2 > ... >
ct, if ciis in level Lx, then ci+1will be in level Lx+1 for all 1 ≤ i ≤ t − 1. In Figure
d1= < c0, c1, c2, c4 >;
d2= < c0, c1, c3, c5 >;
d3= < c0, c1, c3, c6, c7 >.
There are five concept levels that can be extracted. At each level Li, the horizontal
order of concepts on the list is sorted from left to right according to the frequency of the concepts occurrence in descriptors:
L0 = {c0};
L1 = {c1};
L2 = {c3, c2};
L3 = {c4, c5, c6};
L4 = {c7}
Please note that the conceptc0, books, is the common root concept and we assign
c0 in L0. In this paper, we ignore c0 and start the meaningful concepts from L1.
Therefore, the concept structure starts fromL1toLp.