Temat: user intention - Prolib Integro

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Tytuł:: A novel approach to hierarchical contextual bipolar queries : a winnow operator approach
Autorzy:: Kacprzyk, Janusz
Zadrożny, Sławomir
Tematy:: database query
bipolar query
context
fuzzy logic
user intention
user preferences; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Instytut Badań Systemowych PAN
Powiązania:: https://bibliotekanauki.pl/articles/2183491.pdf Link otwiera się w nowym oknie
Opis:: We propose a new approach to the bipolar database queries, which involve a necessary (required) and optional (desired) conditions, connected with a non-conventional aggregation operator “and possibly”, combined with a context, exemplified by “find houses which are cheap and – with respect to other houses in town – possibly close to a railroad station”. We use our winnow operator based interpretation of the bipolar queries. We assume that the query, posed by the human user, involves terms, which do not directly relate to attributes, and which are then to be decoded using a concept of a query hierarchy, leading to the queries, which involve terms directly related to attribute values. The original query is considered to be of level 0, at the bottom of the precisiation hierarchy, then its required and optional parts are assumed to be bipolar queries themselves, both accounting for context. The precisiation proceeds further, to level 1 queries, level 2, etc. A real estate related example is provided as illustration.
Dostawca treści:: Biblioteka Nauki

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Tytuł:: DIARec : dynamic intention-aware recommendation with attention-based context-aware item attributes modeling
Autorzy:: Vaghari, Hadise
Aghdam, Mehdi Hosseinzadeh
Emami, Hojjat
Tematy:: unified recommender system
user intention
context awareness
attention mechanism
collaborative projection
item attribute relation; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/59606695.pdf Link otwiera się w nowym oknie
Opis:: Recommender systems (RSs) often focus on learning users’ long-term preferences, while the sequential pattern of behavior is ignored. On the other hand, sequential RSs try to predict the next action by exploring relations between items in a user’s last interactions but do not consider the general preference. Recently, the performance of RSs has increased by unifying these two types of paradigms. However, existing methods still have two limitations. First, the user’s behavior uncertainty impedes precise learning of preferences. Second, being unable to understand the semantics of items makes the effect of the same item considered in the same way. These limitations jointly prevent RS from learning multifaceted preferences to capture the actual intentions of users. Existing methods have not properly addressed these problems since they ignore context-aware interactions between the user and item in terms of the links between the user and item attributes and sequential user actions over time. To address these challenges, this paper proposes a novel model, called the Dynamic Intention-Aware Recommendation with attention-based context-aware item attributes modeling (DIARec), which is capable of determining users’ preferences based on their goal intention, taking into account the influence of various item features on user decision-making in their current context. Specifically, to model users’ dynamic intentions, we introduce a dynamic intent-aware module to represent the hierarchical relations between items and their attributes in a given session. Experiments on benchmark datasets indicate that the proposed model DIARec outperforms other stateof-the-art methods.
Dostawca treści:: Biblioteka Nauki

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