idea报错找不到jar包问题

本文详细解释了在使用Maven的项目中,如何通过父工程的mvn install操作解决子工程无法找到依赖jar包的问题。这是一篇针对Maven依赖管理的实用指南,适合所有遇到类似问题的开发者。

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idea报错找不到jar包问题

我的项目用的maven依赖传递,父工程要进行mvn install一下,子工程才能找到依赖

### RRF Algorithm in Information Retrieval The **Rank-Biased Overlap (RBO)** and other fusion techniques are often used to combine multiple rankings effectively, but another notable approach is the **Reciprocal Rank Fusion (RRF)** algorithm. The Reciprocal Rank Fusion method addresses issues related to rank aggregation by assigning higher weights to top-ranked items while ensuring diversity across different ranking sources[^1]. In its simplest form, the RRF formula computes fused ranks as follows: For each item \( i \), calculate: \[ \text{Score}(i) = \frac{1}{(k + r_1(i))} + \frac{1}{(k + r_2(i))} + ... + \frac{1}{(k + r_n(i))} \] Where: - \( k \): A constant parameter typically set between 40–60. - \( r_j(i) \): The position of item \( i \) in the j-th ranked list. This ensures that even lower-ranked results from one source may still contribute significantly when they appear highly ranked in others[^2]. #### Implementation Example Below demonstrates how an implementation might look using Python pseudocode for combining two lists with reciprocal rank fusion logic applied: ```python def reciprocal_rank_fusion(lists_of_ranks, k=60): scores = {} for lst_idx, current_list in enumerate(lists_of_ranks): for pos, doc_id in enumerate(current_list, start=1): if doc_id not in scores: scores[doc_id] = 0 scores[doc_id] += 1 / (k + pos) sorted_scores = dict(sorted(scores.items(), key=lambda item: -item[1])) return list(sorted_scores.keys()) ``` Here we define `reciprocal_rank_fusion` which takes several pre-sorted document ID arrays along with optional tuning factor 'k'. It iterates through all given ordered sets calculating their contribution according to defined mathematical model before returning final merged order respecting computed score values per entry found within input collections provided earlier mentioned context about machine learning applications involving sensor modality contrastive alignment could also potentially leverage similar principles albeit adapted specifically towards handling cross-domain feature extraction tasks instead traditional text-based search engine optimization scenarios described previously here today regarding general purpose classifiers like those implemented via specialized programming languages such as APL where simplicity meets power allowing rapid prototyping complex statistical models including ones based off probabilistic assumptions underlying bayesian theory itself.[^3][^4]
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