Novelty recommendation sustains long-term user engagement by exposing users to content that is both relevant and meaningfully different from their recent consumption. In large-scale e-commerce, this requires composing coherent yet non-redundant recommendation lists, a task fundamentally constrained by the relevance-diversity trade-off. Large language models (LLMs) offer a unified generative paradigm for inferring user intent and producing semantically coherent candidates, yet industrial deployment faces two critical challenges: (i) scarce supervision for modeling novelty transitions and diversity-aware list construction, and (ii) reward granularity mismatch, where standard RL assigns coarse sequence-level rewards that fail to capture item-level redundancy and complementarity. We present BALANCE, a hierarchical reasoning-and-generation framework that decomposes novelty recommendation into three structured stages: generating a Novelty Tag for exploration direction, refining an Interest Topic for intent specification, and constructing a Recommendation List for facet coverage. We address data scarcity through a self-reflection pipeline that synthesizes high-quality supervision by integrating real behavior logs with structured rationales. We resolve granularity mismatch through Sequence-Item Policy Optimization (SIPO), which jointly optimizes sequence- and item-level objectives via granularity-aware advantage fusion. Extensive offline experiments and online A/B test on the JD.com recommender system, validate the performance of our method, highlighting its superior novelty and diversity without compromising relevance.

Service composition dynamically integrates various services from multiple providers to meet complex user requirements. However, most existing methods assume centralized control over all services, which is often unrealistic because providers typically prefer to independently manage their own services, posing challenges to the application of traditional methods. Collaborative service composition offers a solution by enabling providers to work together to complete service composition. However, this approach also faces its own challenges. Driven by self-interest, providers may be reluctant to offer services needed by others, and due to business competition, they may wish to share as few services as possible (where sharing services means disclosing service information to other providers). To address these challenges, we propose a novel collaborative service composition approach that comprehensively considers each provider’s self-interest and achieves service composition with minimal service sharing. First, we introduce a “self-interest degree” model to capture providers’ self-interest. This behavior may lead to service refusal, so we design a service availability prediction method based on a reputation model to minimize rejections. Then, we propose a decentralized service composition method. It utilizes historical composition records to mine empirical rules between requirements and services, constructing a correlations matrix, and collaboratively trains a multi-label classification model with other providers under a distributed federated learning framework. Combining the matrix and model outputs, we design a service composition method and a node coordination protocol that completes service composition with minimal service sharing. Experimental results demonstrate the effectiveness of the proposed method in capturing providers’ self-interest and showcase its superior performance compared to existing methods.

Quality-of-service (QoS)-aware service composition aims to aggregate multiple existing services to meet users' complex functional and nonfunctional requirements that cannot be met by simple services. The accumulation of user tasks and service composition solutions makes it possible to mine empirical rules from those historical compositions to reduce the search space and thus improve the composition efficiency. Traditional empirical rule-based methods focus on mining with well-designed rules, ignoring the underlying correlations between tasks and between services. Meanwhile, infrequently used services are not valued by these methods, but these services may still be used in constructing optimal service solutions. In addition, many methods use reinforcement learning to compose services to efficiently construct service solutions, but they do not achieve the same effect as traditional metaheuristic methods. In view of the above shortcomings, considering the ability of graphs to express relationships, we first construct tasks and services as graphs and then use a graph neural network (GNN) to mine underlying correlations and predict the probability that each service will be used to construct the solution corresponding to the task. Next, based on these high-probability services, we utilize pointer network (PN)-based reinforcement learning to efficiently construct the initial service solution. The PN is often used to solve combinatorial optimization problems and is noninferior to metaheuristics for small-scale data. To increase the generalization ability of the network, we superimpose another layer on the PN. Finally, to take advantage of infrequently used services, we use the local whale optimization algorithm (WOA) to fine-tune the initial solution and obtain a high-quality solution. The experimental results show that our approach outperforms several existing methods in terms of both composition efficiency and solution quality.

Many cross-domain and cross-region services and applications have flooded the Internet, such as API services, IoT services, etc. These services are interconnected and form a new service ecosystem, the Internet of Services (IoS). Through IoS, service providers have a broader platform to deliver their services to users. Users can ask for a composite service solution without finding multiple suitable services themselves. However, IoS requires a standard modeling language and methodology to provide developers with a unified understanding of IoS. This modeling methodology should guide developers through the IoS modeling task. Therefore, we propose an Open System Architecture for the Internet of Services (IoS-OSA). We divide IoS into a three-dimensional cube according to layers, views, and lifecycle dimensions, with 64 different views. It integrates the existing scattered work of IoS into a unified framework and introduces the content description and the recommended modeling specification of each view. Finally, we propose the corresponding modeling methods for several key views in IoS-OSA such as service ecosystem, value/quality/capability (VQC) of IoS, etc., which improve the overall architecture of IoS.

Currently, there is a large amount of information about user requirements and service in natural language. How to measure the semantic similarity between user requirements and service description is a critical issue in service recommendation and service solution construction. In this paper, we propose a service recommendation method based on the improved Weighted-Removal(WR) sentence embedding to solve the shortcomings of traditional information retrieval methods. After data preprocessing, we use the GloVe method to obtain the word vectors and use the improved WR sentence embedding method to obtain the sentence vectors. The similarity between the vectors can be better measured. The experimental results show that the proposed improved WR method is significantly better than the traditional methods in terms of recommendation accuracy, richness, and ranking.

Recommending helpful and interesting scholarly papers for researchers from a large number of scholarly papers is the main way to improve research efficiency. Traditional collaborative filtering or content-based recommendation methods do not have a better-fused knowledge graph and have method bottlenecks such as cold start and poor interpretation. Based on the knowledge-aware path recurrent network (KPRN), this paper proposes a method for recommending scholarly papers that combines user preferences and knowledge graph path information. Firstly, a delayed extension bi-directional breadth-first search path algorithm is proposed to find the path between two nodes in the knowledge graph with low time complexity. Then, the user preference vector is generated by the user's historical paper operation. Finally, the LSTM cyclic neural network model is used to extract the information of multiple paths and combine it with user preferences to obtain the list of recommended papers. The experimental results show the validity and good interpretability of this method.