# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Sequential Recommendation
#### 3.2.3 Multi-Objective Sequence Recommendation  
Researchers have recognized that in addition to relevance, diversity and novelty are also important objectives in recommender systems. This implies that these systems should not only recommend items that users are likely to enjoy but also those they have not previously encountered. In the realm of multiobjective sequence recommendation, understanding and modelling user behaviors is one of the core tasks. Traditional methods often overlook the personalized needs of users when addressing different learning objectives [95]. As time has evolved, researchers have started to realize that user interests are not monolithic and often span multiple domains, prompting the emergence of a series of methods. These methods attempt to capture this diversity by encoding the target user's sequential behavior into different interest representation vectors [96]. In simple terms, the input is subjected to grouped convolution operations, which are determined by coupling coefficients obtained via an iterative dynamic routing process and subsequent weighted embedding vectors. Through such convolutional operations, each group of outputs can be regarded as an embedding vector corresponding to a specific interest. Complexity increases when users exhibit a diverse array of behavioral sequences in real-world applications, such as clicks, shares, and purchases. Researchers need to model multiple types of behavioral sequences to uncover users' genuine preferences more comprehensively [97]. This issue also underscores the critical importance of explicitly considering users' historical interactions to enhance performance [98]. Further studies have revealed that the actions within a user's behavioral sequence are often heterogeneous and polysemic [99, 100]. To capture the underlying intents of these actions more effectively, researchers have started assigning varying weights to different types of behaviors. For instance, a purchase action is typically considered a stronger indicator of a user's genuine interest and preference for a product or service compared to a mere click action. Therefore, distinguishing different types of behaviors becomes particularly critical [101]. This concept has been concretely implemented in the CBS model. CBS categorizes the given behavioral sequence into target sequences and supporting sequences based on the types of behaviors observed [102]. This enables the target sequences that are closely related to the most predictive types of behavior (e.g., purchasing) to be highlighted. The authors proposed a novel approach that integrates a basket representation into a recurrent layer to capture sequential effects. The implicit recurrent representation of \(\text{h}_{t}\) is presented as shown in Equation (12). \[h_{t}=g(\Phi_{b}\ b_{t}+\Phi_{h}h_{t-1}+\Omega_{h}) \tag{12}\]  
This approach takes both temporal ordering and continuity into account. The formula explicitly incorporates basket representation, placing particular emphasis on the influence that user choices at specific time steps exert on the hidden state. This distinguishes it from more traditional sequence recommendation methods. A similar line of thinking has also been implemented in BINN [103]. BINN aims to capture users' current interests by utilizing all types of behaviors, such as clicks, purchases, and favorites, while exclusively employing behaviors related to purchases (e.g., buying, adding to cart, and favoriting) to reflect users' long-term preferences. In another study [104], the authors attempted to integrate the specific representations of each type of behavior with the corresponding item embedding vectors, aiming to capture users' interests more comprehensively. The researchers integrated a masked beam search and determinantal point process (DPP) selection to produce a high-quality and diversified bundle list with an appropriate bundle size. The formula is presented in Equation (13).