46 B. Smyth query or profile. The simplest form of case-based recommendation is presented in Fig- ure 11.2. In this figure we use the example of a digital camera recommender system, with the product case base made up of detailed descriptions of individual digital cam- eras. When the user submits a target query--in this instance providing a relatively vague description of their requirements in relation to camera price and pixel resolution--they re presented with a ranked list of k recommendations which represent the top k most milar cases that match the target query As a form of content-based recommendation Product Case-Base Knowledge Case Price: 1000 Pixel: 6 C, sim(t, C,)1, CIsm(t, c,)), 11. 2. In its simplest form a case-based recommendation system will nk product by comparing the user's target query to the descriptions of products In Its ca base using similarity knowledge to identify products that are close matches to the target query (see, for example, [5, 26, 63, 78, 94] and also Chapter 10 [69] of this book) case-base recommenders generate their recommendations by looking to the item descriptions, with items suggested because they have similar descriptions to the user's query. There are two important ways in which case-based recommender systems can be distinguished from other types of content-based systems: (1)the manner in which products are rep- resented; and (2) the way in which product similarity is assessed. Both of these will be discussed in detail in the following sections. 11.2.1 Case Representation Normally content-based recommender systems operate in situations where content items are represented in an unstructured or semi-structured manner. For example, the News Dude content-recommender. which recommends news articles to users. assumes346 B. Smyth query or profile. The simplest form of case-based recommendation is presented in Fig￾ure 11.2. In this figure we use the example of a digital camera recommender system, with the product case base made up of detailed descriptions of individual digital cam￾eras. When the user submits a target query—in this instance providing a relatively vague description of their requirements in relation to camera price and pixel resolution—they are presented with a ranked list of k recommendations which represent the top k most similar cases that match the target query. As a form of content-based recommendation Product Case-Base Price: 1000 Pixel: 6 Target Query, t Case Retrieval Similarity Knowledge c1,…cn Product Recommendations c1 {sim(t, c1)}, : ck {sim(t, c1)}, c1,…ck Fig. 11.2. In its simplest form a case-based recommendation system will retrieve and rank product suggestions by comparing the user’s target query to the descriptions of products stored in its case base using similarity knowledge to identify products that are close matches to the target query. (see, for example, [5, 26, 63, 78, 94] and also Chapter 10 [69] of this book) case-based recommenders generate their recommendations by looking to the item descriptions, with items suggested because they have similar descriptions to the user’s query. There are two important ways in which case-based recommender systems can be distinguished from other types of content-based systems: (1) the manner in which products are rep￾resented; and (2) the way in which product similarity is assessed. Both of these will be discussed in detail in the following sections. 11.2.1 Case Representation Normally content-based recommender systems operate in situations where content items are represented in an unstructured or semi-structured manner. For example, the NewsDude content-recommender, which recommends news articles to users, assumes